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REVIEW 4 major objections 4 minor 200 references

Learning Text Styles: A Study on Transfer, Attribution, and Verification

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This thesis argues that text style analysis — transferring style, attributing authorship, and verifying authorship — can be unified around parameter-efficient, interpretable methods: per-attribute adapters on frozen pre-trained models…

desk verdict A compilation PhD thesis whose headline Adapter-TST claims rest on a circular evaluator; the reproducibility survey is the chapter worth keeping. read the letter →

arxiv 2507.16530 v1 pith:YG6CJQVY submitted 2025-07-22 cs.CL

Zhiqiang Hu This is my paper
classification cs.CL
keywords textstyletransferauthorshipattributionverificationparameter-efficientfine-tuningadaptermodulescontrastivelearningrepresentationdisentanglementinstructiontuning
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This thesis argues that three classic problems in computational text style — changing a text's style, identifying who wrote it, and deciding whether two texts share an author — can all be handled by parameter-efficient, interpretable methods rather than by full fine-tuning of ever-larger language models. The central claim is a paradigm: lightweight bottleneck adapters, inserted into a frozen pre-trained model, can each capture a single stylistic attribute so that one model transfers sentiment, tense, and voice at roughly a fifth of the training cost of full fine-tuning; a contrastive objective combined with mutual-information minimization can separate topic from authorial style, making attribution robust to topic shift; and instruction fine-tuning can make authorship verification both accurate and transparent because the model must justify its verdict. If the claims hold, style-aware NLP — from personalized text generation to forensic linguistics — becomes cheaper and its decisions become auditable by humans.

What carries the argument

The load-bearing mechanism is the attribute-specific bottleneck adapter: a down-projection, a nonlinearity, an up-projection, and a residual skip, injected once into each transformer layer of a frozen pre-trained model. Wiring these adapters in parallel makes one model produce multiple target styles at once, while stacking them performs compositional editing. The attribution machinery is a two-stage objective: supervised contrastive loss pulls together texts by the same author or region, and a variational mutual-information upper bound (vCLUB) pushes the content encoder's representation away from the style encoder's so that topic information cannot leak into the attribution signal. The verification machinery is instruction fine-tuning via LoRA, which trains the model to emit the classification decision and its linguistic explanation in a single generated response.

What would settle it

Run the Adapter-TST evaluation on the StylePTB subsets with human content-preservation ratings on the same outputs that the automatic classifier scores: if transfer accuracy rises while human-judged content preservation falls, the efficiency claim rests on a metric artifact rather than genuine transfer quality. For ContrastDistAA, train the model with the vCLUB disentanglement term removed and compare on a held-out-topic split of CCAT50 or the regional dataset, matching topics between training and test only in the control condition — if accuracy is unchanged, the mutual-information step is not doing the claimed separation work.

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Extended reading notes

Core claim

On the thesis's own terms, the discovery is that transfer, attribution, and verification are facets of one underlying problem — isolating the stylistic signal from the content signal — and that a small trainable surface on a frozen pre-trained model solves it across all three tasks. Adapter-TST adds one bottleneck adapter per stylistic attribute to BART or T5, freezing the backbone; parallel-connected adapters generate several target-style outputs at once and stacked adapters compose multiple styles in one edit, reporting state-of-the-art results on sentiment transfer, multiple-attribute outputs, and compositional editing at 80 percent lower computational cost than full fine-tuning. ContrastDistAA trains a style encoder with supervised contrastive loss, then minimizes a variational contrastive log-ratio upper bound between style and content representations so that authorship judgments survive topic shifts, and it extends attribution to regional linguistic styles with a new regional-tweets dataset. InstructAV instruction-tunes a large language model with low-rank adapters to produce the authorship verdict together with a human-interpretable linguistic rationale, reporting accuracy above ChatGPT and specialized baselines and explanations whose quality tracks the classification accuracy.

Load-bearing premise

The load-bearing premise is that the automatic evaluation protocol — a pre-trained attribute classifier measuring transfer accuracy, with perplexity standing in for fluency — captures true style-transfer quality; if that classifier rewards superficial rewrites rather than genuine style change, the claimed superiority of Adapter-TST over its baselines is not established, and a related premise holds that minimizing vCLUB between style and content representations removes topic information without discarding stylistic signal, which the thesis asserts but never tests on a held-out-topic benchmark.

Editorial extensions

If this is right

  • A single frozen pre-trained model with per-attribute adapters can perform multi-attribute and compositional style transfer, replacing the practice of fine-tuning a separate model per style and cutting training cost by roughly 80 percent.
  • Attribution models can be made robust to topic shift by explicitly minimizing mutual information between content and style representations, and the same tool extends attribution to regional dialects.
  • Authorship verification can be jointly accurate and explainable: instruction-tuned models produce rationales whose assessed quality correlates with classification accuracy, a property few-shot prompting baselines lack.
  • With sufficient task-specific data, parameter-efficient fine-tuning lets smaller open models match or beat much larger closed models on specific reasoning and style benchmarks.
  • The released benchmarks and datasets — the reproduced 19-method style-transfer study, Math10K, Commonsense170K, and the regional-tweets dataset — give the field a shared ground for comparing future methods.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The three frameworks are composable in a way the thesis leaves untested: a style representation learned by ContrastDistAA's disentangled encoder could serve as the attribute signal inside Adapter-TST, potentially improving compositional editing by suppressing topic leakage; this combination is a natural next experiment.
  • The efficiency claim has a deployment corollary the author does not draw: because all attributes live in adapters on one frozen backbone, a single served model could add or retire stylistic attributes by swapping modules, which full fine-tuning cannot do without retraining the whole checkpoint.
  • The regional-attribution dataset invites a check the thesis does not run: whether the learned region embeddings track non-geographic cultural or demographic variables, which would reveal whether the regional signal is style or sociolinguistic content.
  • A sceptical extension of the verification claim: if InstructAV's explanations are used as evidence in forensic settings, one would want a direct stress test where the explanation is generated first and the verdict must follow from it, rather than both being produced jointly.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. This PhD thesis is organized into three parts. Part I presents a survey and reproducibility study of 19 text style transfer (TST) algorithms, the LLM-Adapters parameter-efficient fine-tuning (PEFT) framework, and Adapter-TST, a multi-attribute TST model built from attribute-specific neural adapters. Part II introduces ContrastDistAA, a contrastive-learning and mutual-information method for authorship attribution (AA) that is designed to disentangle topic content from stylistic features. Part III presents InstructAV, an instruction-tuned LLM for authorship verification (AV) that combines classification with linguistic explanations. The abstract claims that Adapter-TST outperforms state-of-the-art baselines while reducing computational cost by 80%, that ContrastDistAA achieves state-of-the-art accuracy under topic shifts, and that InstructAV outperforms ChatGPT and specialized baselines.

Significance. The thesis has concrete strengths: a large reproducibility study with released code, an open-source PEFT framework (LLM-Adapters), a newly collected Regional Tweets dataset for regional AA, and human evaluation for both InstructAV and a subset of Adapter-TST. If the headline claims were fully supported, the three frameworks would be useful contributions to their respective subfields. However, as presented, several central claims are not established by the reported evidence. Adapter-TST's automatic evaluation is circular because the classifier used to compute transfer accuracy is the same kind as the one used in the training loss; the 80% cost reduction is not directly measured; ContrastDistAA is not tested under a topic-shift protocol despite that being its motivation; and InstructAV's explanation labels are generated by the same type of LLM used as a baseline. These issues materially weaken the paper's central assertions, though they are reparable with additional experiments and rephrasing.

major comments (4)
  1. [Sec. 5.2.3 and Sec. 5.3.2] The TextCNN attribute classifier D used in the classification loss Lcls (Eq. 5.1) is the same type of pre-trained classifier that is later used to compute transfer accuracy ACC in the automatic evaluation. Because the adapters are trained to maximize the probability assigned by D, the ACC values in Tables 5.2–5.4 may reflect the model's success at exploiting D's decision boundary rather than genuine stylistic transfer. The human evaluation in Table 5.5 covers only 200 sentences from one of the four StylePTB subsets (Tense-Voice), so the superiority claims on the other three multi-attribute subsets rest entirely on the circular automatic metric. I recommend reporting ACC with a classifier trained independently of the one used in Lcls, or providing per-attribute human evaluation for all four subsets, before claiming that Adapter-TST outperforms Style Transformer baselines.
  2. [Sec. 5.3.2 and Sec. 1.3.1] The G-score is defined as the geometric mean of ACC, BERTscore, and 1/PPL, and the inverse-perplexity term can be inflated by short, conservative, or content-dropping outputs. Adapter-TST-T5 achieves very low PPL values (e.g., 1.7–3.8 in Tables 5.3 and 5.4) even when attribute accuracy is low (e.g., 48.9% for Tense-PP-Front↔Back in Table 5.3), which suggests that the high G-scores may partly reward conservative generation. In addition, the abstract's claim of '80% lower computational cost' is never directly measured; Sec. 5.2.1 reports parameter counts only, and no wall-clock time, FLOPs, or energy measurements appear in the chapter. The computational-cost claim should be either substantiated with direct measurements of training/inference cost or rephrased as parameter efficiency.
  3. [Sec. 6.1 and Sec. 6.3] ContrastDistAA is introduced as a solution for performance degradation under topic shifts and the abstract claims state-of-the-art accuracy under topic shifts, but the experimental protocol does not contain a topic-shift condition. The four datasets in Table 6.1 are split by standard random partitions, not by held-out topics, so the aggregate F1 scores in Table 6.3 do not measure robustness to unseen topics. The vCLUB disentanglement loss in Eq. (6.3) is intended to remove topic information, but its impact is only shown through overall F1 and t-SNE visualizations (Fig. 6.2). I recommend adding a topic-shift evaluation, for example training on one set of news topics in CCAT50 and testing on another, or constructing a topical split of Regional Tweets.
  4. [Sec. 7.2.1] The explanation labels used to fine-tune InstructAV are generated by an LLM (ChatGPT), and the same type of model is used as a baseline. This creates a potential circularity in the explanation-quality comparison: the model is trained to reproduce the distribution of the teacher's explanations and then compared against that teacher. The human evaluation in Table 7.4 mitigates this concern, but the report does not state whether the evaluators were shown the training labels or provide inter-annotator agreement statistics. To fully support the claim that InstructAV produces 'dependable linguistic explanations,' the authors should report human agreement and clarify the relationship between the training labels and the evaluation protocol.
minor comments (4)
  1. [Chapter 4 and Sec. 4.5] The framework is referred to both as 'LLM-Adapter' and 'LLM-Adapters' in different places; please standardize the name throughout.
  2. [Table 3.4] The rows labeled 'Human0' through 'Human3' are human references but the caption does not explain this; please add a clarifying note.
  3. [Table of Contents and Sec. 1.3.2] The chapter title contains the typo 'Disentaglement'; please correct to 'Disentanglement'.
  4. [Sec. 5.2.2] The Stack connection is used only at inference, with adapters trained under the Parallel connection, so the compositional editing results in Table 5.4 evaluate a configuration that was never jointly trained; please discuss the validity of this procedure and whether a joint training recipe for stacking was considered.

Circularity Check

1 steps flagged · score 6.0 of 10

Adapter-TST's reported transfer accuracy is computed with the same attribute classifier that supplies its training loss, making the headline multi-attribute improvement partially forced.

  1. fitted input called prediction [Sec. 5.2.3 (Eq. 5.1) and Sec. 5.3.2 (Automatic Evaluation)]
    "To this end, we first pre-train a TextCNN-based [75] binary attribute classifier D for each attribute, then apply the pre-trained attribute classifiers to guide the updates of adapters' parameters such that the output sentence is predicted to be in the target style: Lcls = −E(x,y)∼D[logP (yt|x′)] (5.1). ... An attribute classifier is first pre-trained to predict the attribute label of the input sentence. The classifier is subsequently used to approximate the style transfer accuracy (ACC) of the sentences' transferred attributes by considering the target attribute value as the ground truth."

    The same kind of TextCNN attribute classifier that provides the classification loss Lcls in Eq. 5.1 is also used, in Sec. 5.3.2, to compute the reported transfer accuracy ACC. Since Lcls is optimized by policy gradient to maximize D's probability of the target label, the training objective directly maximizes a soft proxy of the ACC evaluation. High ACC therefore indicates that the adapters have learned to satisfy the very classifier they were trained against, not an independent confirmation of human-meaningful style transfer. The headline G-score is the geometric mean of ACC, BERTscore, and 1/PPL, so the claimed 'outperforms state-of-the-art baselines' on multi-attribute TST is partially forced by construction.

full rationale

The clearest circular step is in Chapter 5: the attribute classifier D that defines the training loss Lcls in Eq. 5.1 is the same type of classifier that computes ACC in Sec. 5.3.2, and ACC is a component of the G-score used to declare Adapter-TST superior. This makes the central multi-attribute transfer claim partially reduce to a fitted-input called prediction. The other two pillars are not shown to be circular from the provided text: ContrastDistAA's vCLUB disentanglement objective is an architectural choice rather than an evaluation identity, and InstructAV's explanation-label pipeline is not fully quoted in the available text, so no circularity can be verified there. LLM-Adapters is evaluated on external reasoning benchmarks, and its fine-tuning data does not include the test sets it is measured against, so that chapter is self-contained. The human evaluation in Table 5.5 provides some independent support for one dataset, but the automatic evaluation covering all four multi-attribute subsets is compromised by the shared classifier. Because the circularity affects the headline claim of one of the three pillars rather than the entire thesis, a score of 6 is appropriate.

Assumptions & free parameters 7 free parameters · 5 assumptions · 0 invented entities

The technical claims rest on standard machine learning machinery plus strong domain assumptions. No free parameters are physical constants; they are hyperparameters and validation choices. The most consequential assumptions are that style and content are separable, that a style classifier is a valid judge of transfer, and that PPL is a valid fluency term in G-score. No invented physical or linguistic entities are introduced; the new objects are model components and one dataset.

free parameters (7)
  • Adapter-TST loss weight lambda = tuned from {0.9, 1}
    Balances reconstruction and classifier losses; chosen by validation, not derived.
  • Adapter-TST bottleneck size Hd = 64
    Projection dimension, set to keep adapter parameters small; no principled selection.
  • LLM-Adapters prefix virtual tokens vt = 10
    Selected from {10, 20, 30, 40} on math reasoning accuracy.
  • LLM-Adapters LoRA rank r = 32
    Selected from {4, 8, 16, 32}; larger rank improved accuracy in the empirical study.
  • LLM-Adapters series and parallel bottleneck size = 256
    Selected from {64, 128, 256, 512} on math reasoning accuracy.
  • ContrastDistAA learning rate = 1e-3
    Set for both training stages; not derived from theory.
  • ContrastDistAA batch size = 32
    Set for both training stages; not derived from theory.
assumptions (5)
  • domain assumption Style and content are separable in latent space.
    ContrastDistAA's disentanglement premise; stated in Sec. 6.1 and 6.2 with no proof that style is independent of topic.
  • ad hoc to paper A classifier trained on style labels is a valid judge of transfer accuracy.
    Adapter-TST uses TextCNN classifiers both in Lcls and ACC; see Sec. 5.2.3 and 5.3.2.
  • domain assumption Perplexity of a pre-trained language model is a valid fluency component in a composite G-score.
    G-score uses 1/PPL as fluency; low PPL can reflect copying or short output rather than good transfer.
  • standard math CLUB provides a tight enough upper bound on mutual information with the learned variational distribution.
    Invokes the CLUB estimator [17] as established; assumes q_theta is adequately trained.
  • domain assumption Pre-trained models such as BERT, BART, and T5 encode stylistic information useful for transfer and attribution.
    Used throughout the thesis as the backbone for all proposed methods; no causal validation is provided.

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Cite this review

Pith. "Pith review of Learning Text Styles: A Study on Transfer, Attribution, and Verification." pith.science (2026). https://pith.science/paper/YG6CJQVY

@misc{pith2026250716530,
  author       = {Pith},
  title        = {Pith review of: Learning Text Styles: A Study on Transfer, Attribution, and Verification},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YG6CJQVY}},
  note         = {Machine review of arXiv:2507.16530}
}
read the original abstract

This thesis advances the computational understanding and manipulation of text styles through three interconnected pillars: (1) Text Style Transfer (TST), which alters stylistic properties (e.g., sentiment, formality) while preserving content; (2)Authorship Attribution (AA), identifying the author of a text via stylistic fingerprints; and (3) Authorship Verification (AV), determining whether two texts share the same authorship. We address critical challenges in these areas by leveraging parameter-efficient adaptation of large language models (LLMs), contrastive disentanglement of stylistic features, and instruction-based fine-tuning for explainable verification.

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Works this paper leans on

200 extracted references · 31 canonical work pages

  1. [1]

    Classification for authorship of tweets by com- paring logistic regression and naive bayes classifiers

    Opeyemi Aborisade and Mohd Anwar. “Classification for authorship of tweets by com- paring logistic regression and naive bayes classifiers”. In:2018 IEEE International Con- ference on Information Reuse and Integration (IRI). IEEE. 2018, pp. 269–276

    2018

  2. [2]

    Intrinsic Dimensionality Ex- plains the Effectiveness of Language Model Fine-Tuning

    Armen Aghajanyan, Luke Zettlemoyer, and Sonal Gupta. “Intrinsic Dimensionality Ex- plains the Effectiveness of Language Model Fine-Tuning”. In: Annual Meeting of the Association for Computational Linguistics. 2020

    2020

  3. [3]

    The Topic Con- fusion Task: A Novel Evaluation Scenario for Authorship Attribution

    Malik Altakrori, Jackie Chi Kit Cheung, and Benjamin C. M. Fung. “The Topic Con- fusion Task: A Novel Evaluation Scenario for Authorship Attribution”. In: Findings of the Association for Computational Linguistics: EMNLP 2021 . Punta Cana, Dominican Republic: Association for Computational Linguistics, Nov. 2021, pp. 4242–4256. DOI: 10.18653/v1/2021.finding...

    work page doi:10.18653/v1/2021.findings-emnlp.359 2021

  4. [4]

    Neural Machine Translation by Jointly Learning to Align and Translate

    Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. “Neural Machine Translation by Jointly Learning to Align and Translate”. In:3rd International Conference on Learn- ing Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings. Ed. by Yoshua Bengio and Yann LeCun. 2015

    2015

  5. [5]

    Mutual information neural estimation

    Mohamed Ishmael Belghazi et al. “Mutual information neural estimation”. In: Interna- tional conference on machine learning. PMLR. 2018, pp. 531–540

    2018

  6. [6]

    Generalizing unmasking for short texts

    Janek Bevendorff et al. “Generalizing unmasking for short texts”. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019, pp. 654–659

    2019

  7. [7]

    The importance of suppressing domain style in authorship analysis

    Sebastian Bischoff et al. “The importance of suppressing domain style in authorship analysis”. In: arXiv preprint arXiv:2005.14714 (2020)

    arXiv 2020

  8. [8]

    PIQA: Reasoning about Physical Commonsense in Natural Lan- guage

    Yonatan Bisk et al. “PIQA: Reasoning about Physical Commonsense in Natural Lan- guage”. In: Thirty-Fourth AAAI Conference on Artificial Intelligence. 2020

    2020

Show all 200 references
  1. [9]

    Explainable authorship verification in social media via attention-based similarity learning

    Benedikt Boenninghoff et al. “Explainable authorship verification in social media via attention-based similarity learning”. In: 2019 IEEE International Conference on Big Data (Big Data). IEEE. 2019, pp. 36–45

    2019

  2. [10]

    Similarity learning for authorship verification in social media

    Benedikt Boenninghoff et al. “Similarity learning for authorship verification in social media”. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE. 2019, pp. 2457–2461

    2019

  3. [12]

    Evaluating prose style transfer with the Bible

    Keith Carlson, Allen Riddell, and Daniel Rockmore. “Evaluating prose style transfer with the Bible”. In: Royal Society open science 5.10 (2018), p. 171920. Bibliography 91

    2018

  4. [13]

    Parameter-Efficient Fine-Tuning Design Spaces

    Jiaao Chen et al. “Parameter-Efficient Fine-Tuning Design Spaces”. In: arXiv preprint arXiv:2301.01821 (2023)

    2023 arXiv

  5. [14]

    Adversarial text generation via feature-mover’s distance

    Liqun Chen et al. “Adversarial text generation via feature-mover’s distance”. In: Ad- vances in Neural Information Processing Systems. 2018, pp. 4666–4677

    2018

  6. [15]

    A simple framework for contrastive learning of visual representations

    Ting Chen et al. “A simple framework for contrastive learning of visual representations”. In: International conference on machine learning. PMLR. 2020, pp. 1597–1607

    2020

  7. [16]

    Infogan: Interpretable representation learning by information maximiz- ing generative adversarial nets

    Xi Chen et al. “Infogan: Interpretable representation learning by information maximiz- ing generative adversarial nets”. In: Advances in neural information processing systems 29 (2016)

    2016

  8. [17]

    Club: A contrastive log-ratio upper bound of mutual information

    Pengyu Cheng et al. “Club: A contrastive log-ratio upper bound of mutual information”. In: International conference on machine learning. PMLR. 2020, pp. 1779–1788

    2020

  9. [18]

    Improving Disentangled Text Representation Learning with Information- Theoretic Guidance

    Pengyu Cheng et al. “Improving Disentangled Text Representation Learning with Information- Theoretic Guidance”. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020. Ed. by Dan Jurafsky et al. Associatio...

    2020

  10. [19]

    Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

    Kyunghyun Cho et al. “Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation”. In: Proceedings of the 2014 Conference on Empir- ical Methods in Natural Language Processing (EMNLP). 2014, pp. 1724–1734

    2014

  11. [20]

    BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

    Christopher Clark et al. “BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions”. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Paper...

    2019

  12. [21]

    Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

    Peter Clark et al. “Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge”. In: arXiv:1803.05457v1 (2018)

    2018 arXiv

  13. [22]

    Training verifiers to solve math word problems

    Karl Cobbe et al. “Training verifiers to solve math word problems”. In: arXiv preprint arXiv:2110.14168 (2021)

    2021 arXiv

  14. [23]

    Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation

    Ning Dai et al. “Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation”. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019, pp. 5997–6007

    2019

  15. [24]

    Plug and Play Language Models: A Simple Approach to Con- trolled Text Generation

    Sumanth Dathathri et al. “Plug and Play Language Models: A Simple Approach to Con- trolled Text Generation”. In:8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net, 2020

    2020

  16. [25]

    BERT: Pre-training of Deep Bidirectional Transformers for Lan- guage Understanding

    Jacob Devlin et al. “BERT: Pre-training of Deep Bidirectional Transformers for Lan- guage Understanding”. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technolo- gies, Volume 1 (Long and Sh...

    2019 doi

  17. [26]

    BERT: Pre-training of Deep Bidirectional Transformers for Lan- guage Understanding

    Jacob Devlin et al. “BERT: Pre-training of Deep Bidirectional Transformers for Lan- guage Understanding”. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technolo- gies, Volume 1 (Long and Sh...

    2019

  18. [28]

    Bert: Pre-training of deep bidirectional transformers for language understanding

    Jacob Devlin et al. “Bert: Pre-training of deep bidirectional transformers for language understanding”. In: arXiv preprint arXiv:1810.04805 (2018)

    2018 arXiv

  19. [29]

    Learning stylometric representations for authorship analysis

    Steven HH Ding et al. “Learning stylometric representations for authorship analysis”. In: IEEE transactions on cybernetics 49.1 (2017), pp. 107–121

    2017

  20. [30]

    KronA: Parameter Efficient Tuning with Kronecker Adapter

    Ali Edalati et al. “KronA: Parameter Efficient Tuning with Kronecker Adapter”. In: ArXiv abs/2212.10650 (2022)

    2022 arXiv

  21. [31]

    CharacterBERT: Reconciling ELMo and BERT for Word- Level Open-V ocabulary Representations From Characters

    Hicham El Boukkouri et al. “CharacterBERT: Reconciling ELMo and BERT for Word- Level Open-V ocabulary Representations From Characters”. In:Proceedings of the 28th International Conference on Computational Linguistics. Barcelona, Spain (Online): In- ternational Committee on Com...

    2020 doi

  22. [32]

    BertAA : BERT fine-tuning for Authorship Attribution

    Maël Fabien et al. “BertAA : BERT fine-tuning for Authorship Attribution”. In: Pro- ceedings of the 17th International Conference on Natural Language Processing (ICON). Indian Institute of Technology Patna, Patna, India: NLP Association of India (NLPAI), Dec. 2020, pp. 127–137...

    2020

  23. [33]

    BertAA: BERT fine-tuning for Authorship Attribution

    Maël Fabien et al. “BertAA: BERT fine-tuning for Authorship Attribution”. In: Pro- ceedings of the 17th International Conference on Natural Language Processing (ICON). 2020, pp. 127–137

    2020

  24. [34]

    Learn-to-Share: A Hardware-friendly Transfer Learning Framework Exploiting Computation and Parameter Sharing

    Cheng Fu et al. “Learn-to-Share: A Hardware-friendly Transfer Learning Framework Exploiting Computation and Parameter Sharing”. In: Proceedings of the 38th Inter- national Conference on Machine Learning . Ed. by Marina Meila and Tong Zhang. V ol. 139. Proceedings of Machine Le...

    2021

  25. [35]

    Style Transfer in Text: Exploration and Evaluation

    Zhenxin Fu et al. Style Transfer in Text: Exploration and Evaluation . 2017. arXiv: 1711.06861 [cs.CL]

    2017 arXiv

  26. [36]

    Style transfer in text: Exploration and evaluation

    Zhenxin Fu et al. “Style transfer in text: Exploration and evaluation”. In: Thirty-Second AAAI Conference on Artificial Intelligence. 2018

    2018

  27. [37]

    SimCSE: Simple Contrastive Learning of Sentence Embeddings

    Tianyu Gao, Xingcheng Yao, and Danqi Chen. SimCSE: Simple Contrastive Learning of Sentence Embeddings. 2022. arXiv: 2104.08821 [cs.CL]

    2022 arXiv

  28. [38]

    Survey of the state of the art in natural language generation: Core tasks, applications and evaluation

    Albert Gatt and Emiel Krahmer. “Survey of the state of the art in natural language generation: Core tasks, applications and evaluation”. In:Journal of Artificial Intelligence Research 61 (2018), pp. 65–170

    2018

  29. [39]

    A Neural Algorithm of Artis- tic Style

    Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. “A Neural Algorithm of Artis- tic Style”. In: CoRR abs/1508.06576 (2015). arXiv: 1508.06576. Bibliography 93

    2015 arXiv

  30. [40]

    DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Rep- resentations

    John Giorgi et al. DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Rep- resentations. 2021. arXiv: 2006.03659 [cs.CL]

    2021 arXiv

  31. [41]

    Reinforcement Learning Based Text Style Transfer without Par- allel Training Corpus

    Hongyu Gong et al. “Reinforcement Learning Based Text Style Transfer without Par- allel Training Corpus”. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technolo- gies, Volume 1 (Long and Sh...

    2019

  32. [42]

    Multi-Style Transfer with Discriminative Feedback on Disjoint Cor- pus

    Navita Goyal et al. “Multi-Style Transfer with Discriminative Feedback on Disjoint Cor- pus”. In: Proceedings of the 2021 Conference of the North American Chapter of the As- sociation for Computational Linguistics: Human Language Technologies . Online: As- sociation for Comput...

    2021

  33. [43]

    Assessing the applicability of au- thorship verification methods

    Oren Halvani, Christian Winter, and Lukas Graner. “Assessing the applicability of au- thorship verification methods”. In: Proceedings of the 14th International Conference on Availability, Reliability and Security. 2019, pp. 1–10

    2019

  34. [44]

    A Probabilistic Formulation of Unsupervised Text Style Transfer

    Junxian He et al. “A Probabilistic Formulation of Unsupervised Text Style Transfer”. In: International Conference on Learning Representations (ICLR). 2020

    2020

  35. [45]

    Towards a Unified View of Parameter-Efficient Transfer Learning

    Junxian He et al. “Towards a Unified View of Parameter-Efficient Transfer Learning”. In: International Conference on Learning Representations . 2022. URL: https : / / openreview.net/forum?id=0RDcd5Axok

    2022

  36. [46]

    Towards a unified view of parameter-efficient transfer learning

    Junxian He et al. “Towards a unified view of parameter-efficient transfer learning”. In: arXiv preprint arXiv:2110.04366 (2021)

    2021 arXiv

  37. [47]

    Momentum Contrast for Unsupervised Visual Representation Learn- ing

    Kaiming He et al. “Momentum Contrast for Unsupervised Visual Representation Learn- ing”. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2020, pp. 9726–9735. DOI: 10.1109/CVPR42600.2020

    2020

  38. [48]

    SparseAdapter: An Easy Approach for Improving the Parameter-Efficiency of Adapters

    Shwai He et al. “SparseAdapter: An Easy Approach for Improving the Parameter-Efficiency of Adapters”. In: Findings of the Association for Computational Linguistics: EMNLP

  39. [49]

    Hénaff et al

    Olivier J. Hénaff et al. Data-Efficient Image Recognition with Contrastive Predictive Coding. 2020. arXiv: 1905.09272 [cs.CV]

    2020 arXiv

  40. [50]

    Compacter: Efficient low-rank hypercomplex adapter layers

    James Henderson, Sebastian Ruder, et al. “Compacter: Efficient low-rank hypercomplex adapter layers”. In: Advances in Neural Information Processing Systems. 2021

    2021

  41. [51]

    Authorship attribution with convolutional neural networks and POS-eliding

    Julian Hitschler, Esther Van Den Berg, and Ines Rehbein. “Authorship attribution with convolutional neural networks and POS-eliding”. In: Proceedings of the Workshop on Stylistic Variation. 2017, pp. 53–58

    2017

  42. [52]

    Learning to solve arithmetic word problems with verb categorization

    Mohammad Javad Hosseini et al. “Learning to solve arithmetic word problems with verb categorization.” In: EMNLP. 2014, pp. 523–533

    2014

  43. [53]

    Parameter-Efficient Transfer Learning for NLP

    Neil Houlsby et al. “Parameter-Efficient Transfer Learning for NLP”. In: International Conference on Machine Learning. 2019

    2019

  44. [54]

    Parameter-efficient transfer learning for NLP

    Neil Houlsby et al. “Parameter-efficient transfer learning for NLP”. In: International Conference on Machine Learning. PMLR. 2019, pp. 2790–2799. Bibliography 94

    2019

  45. [55]

    Generating natural language under pragmatic constraints

    Eduard Hovy. “Generating natural language under pragmatic constraints”. In: Journal of Pragmatics 11.6 (1987), pp. 689–719

    1987

  46. [57]

    LoRA: Low-Rank Adaptation of Large Language Models

    Edward J. Hu et al. “LoRA: Low-Rank Adaptation of Large Language Models”. In: ArXiv abs/2106.09685 (2021)

    2021 arXiv

  47. [58]

    TDRLM: Stylometric learning for authorship verification by Topic- Debiasing

    Xinyu Hu et al. “TDRLM: Stylometric learning for authorship verification by Topic- Debiasing”. In: Expert Systems with Applications 233 (2023), p. 120745

    2023

  48. [59]

    Are Current Task-oriented Dia- logue Systems Able to Satisfy Impolite Users?

    Zhiqiang Hu, Roy Kaa-Wei Lee, and Nancy F Chen. “Are Current Task-oriented Dia- logue Systems Able to Satisfy Impolite Users?” In: arXiv preprint arXiv:2210.12942 (2022)

    2022 arXiv

  49. [60]

    Deepstyle: User style embedding for authorship attribution of short texts

    Zhiqiang Hu et al. “Deepstyle: User style embedding for authorship attribution of short texts”. In: Web and Big Data: 4th International Joint Conference, APWeb-WAIM 2020, Tianjin, China, September 18-20, 2020, Proceedings, Part II 4. Springer. 2020, pp. 221– 229

    2020

  50. [61]

    LLM-Adapters: An Adapter Family for Parameter-Efficient Fine- Tuning of Large Language Models

    Zhiqiang Hu et al. “LLM-Adapters: An Adapter Family for Parameter-Efficient Fine- Tuning of Large Language Models”. In: arXiv preprint arXiv:2304.01933 (2023)

    2023 arXiv

  51. [62]

    Text style transfer: A review and experimental evaluation

    Zhiqiang Hu et al. “Text style transfer: A review and experimental evaluation”. In: ACM SIGKDD Explorations Newsletter 24.1 (2022), pp. 14–45

    2022

  52. [63]

    Toward controlled generation of text

    Zhiting Hu et al. “Toward controlled generation of text”. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org. 2017, pp. 1587– 1596

    2017

  53. [64]

    Can Large Language Models Identify Au- thorship? 2024

    Baixiang Huang, Canyu Chen, and Kai Shu. Can Large Language Models Identify Au- thorship? 2024. arXiv: 2403 . 08213 [cs.CL]. URL: https : / / arxiv . org / abs/2403.08213

    2024 arXiv

  54. [65]

    Who Wrote it and Why? Prompting Large-Language Models for Authorship Verification

    Chia-Yu Hung et al. “Who Wrote it and Why? Prompting Large-Language Models for Authorship Verification”. In: arXiv preprint arXiv:2310.08123 (2023)

    2023 arXiv

  55. [66]

    A novel approach of mining write-prints for authorship attribution in e-mail forensics

    Farkhund Iqbal et al. “A novel approach of mining write-prints for authorship attribution in e-mail forensics”. In: digital investigation 5 (2008), S42–S51

    2008

  56. [67]

    Unsupervised controllable text formalization

    Parag Jain et al. “Unsupervised controllable text formalization”. In: Proceedings of the AAAI Conference on Artificial Intelligence. V ol. 33. 2019, pp. 6554–6561

    2019

  57. [68]

    Categorical Reparameterization with Gumbel- Softmax

    Eric Jang, Shixiang Gu, and Ben Poole. “Categorical Reparameterization with Gumbel- Softmax”. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings . OpenReview.net,

    2017

  58. [69]

    Shakespearizing Modern Language Using Copy-Enriched Se- quence to Sequence Models

    Harsh Jhamtani et al. “Shakespearizing Modern Language Using Copy-Enriched Se- quence to Sequence Models”. In: Proceedings of the Workshop on Stylistic Variation . 2017, pp. 10–19

    2017

  59. [70]

    Deep Learning for Text Style Transfer: A Survey

    Di Jin et al. “Deep Learning for Text Style Transfer: A Survey”. In: Computational Linguistics 48.1 (Mar. 2022), pp. 155–205. DOI: 10.1162/coli_a_00426 . URL: https://aclanthology.org/2022.cl-1.6. Bibliography 95

    2022 doi

  60. [71]

    IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation

    Zhijing Jin et al. “IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation”. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-I...

    2019

  61. [72]

    Disentangled Representation Learning for Non-Parallel Text Style Transfer

    Vineet John et al. “Disentangled Representation Learning for Non-Parallel Text Style Transfer”. In: Proceedings of the 57th Annual Meeting of the Association for Compu- tational Linguistics . Florence, Italy: Association for Computational Linguistics, July 2019, pp. 424–434.DO...

    2019 doi

  62. [73]

    Are you robert or roberta? deceiving online authorship attribution models using neural text generators

    Keenan Jones, Jason RC Nurse, and Shujun Li. “Are you robert or roberta? deceiving online authorship attribution models using neural text generators”. In: Proceedings of the International AAAI Conference on Web and Social Media . V ol. 16. 2022, pp. 429– 440

    2022

  63. [74]

    Supervised Contrastive Learning

    Prannay Khosla et al. Supervised Contrastive Learning . 2021. arXiv: 2004 . 11362 [cs.LG]

    2021

  64. [75]

    Convolutional Neural Networks for Sentence Classification

    Yoon Kim. “Convolutional Neural Networks for Sentence Classification”. In: Proceed- ings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2014, pp. 1746–1751

    2014

  65. [76]

    Large language models are zero-shot reasoners

    Takeshi Kojima et al. “Large language models are zero-shot reasoners”. In: arXiv preprint arXiv:2205.11916 (2022)

    2022 arXiv

  66. [77]

    MAWPS: A Math Word Problem Repository

    Rik Koncel-Kedziorski et al. “MAWPS: A Math Word Problem Repository”. In: Pro- ceedings of NAACL. 2016, pp. 1152–1157. URL: https://aclanthology.org/ N16-1136

    2016

  67. [78]

    Parsing algebraic word problems into equations

    Rik Koncel-Kedziorski et al. “Parsing algebraic word problems into equations”. In: Transactions of the Association for Computational Linguistics 3 (2015), pp. 585–597

    2015

  68. [79]

    Different Encoding Approaches for Au- thorship Verification

    Stefanos Konstantinou, A Zinonos, and J Li. “Different Encoding Approaches for Au- thorship Verification”. In: CLEF. 2022

    2022

  69. [80]

    Multiple Text Style Transfer by using Word-level Conditional Gen- erative Adversarial Network with Two-Phase Training

    Chih-Te Lai et al. “Multiple Text Style Transfer by using Word-level Conditional Gen- erative Adversarial Network with Two-Phase Training”. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th Inter- national Joint Conference o...

    2019

  70. [81]

    Multiple-Attribute Text Rewriting

    Guillaume Lample et al. “Multiple-Attribute Text Rewriting”. In: 7th International Con- ference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9,

    2019

  71. [82]

    Albert: A lite bert for self-supervised learning of language rep- resentations

    Zhenzhong Lan et al. “Albert: A lite bert for self-supervised learning of language rep- resentations”. In: arXiv preprint arXiv:1909.11942 (2019)

    2019 arXiv

  72. [83]

    The Power of Scale for Parameter- Efficient Prompt Tuning

    Brian Lester, Rami Al-Rfou, and Noah Constant. “The Power of Scale for Parameter- Efficient Prompt Tuning”. In: ArXiv abs/2104.08691 (2021). Bibliography 96

    2021 arXiv

  73. [84]

    BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

    Mike Lewis et al. “BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension”. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . Online: Association for Computational Linguistic...

    2020

  74. [85]

    Domain Adaptive Text Style Transfer

    Dianqi Li et al. “Domain Adaptive Text Style Transfer”. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th Inter- national Joint Conference on Natural Language Processing (EMNLP-IJCNLP) . 2019, pp. 3295–3304

    2019

  75. [86]

    Delete, Retrieve, Generate: a Simple Approach to Sentiment and Style Transfer

    Juncen Li et al. “Delete, Retrieve, Generate: a Simple Approach to Sentiment and Style Transfer”. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018, p...

    2018

  76. [87]

    Prefix-Tuning: Optimizing Continuous Prompts for Generation

    Xiang Lisa Li and Percy Liang. “Prefix-Tuning: Optimizing Continuous Prompts for Generation”. In: Proceedings of the 59th Annual Meeting of the Association for Compu- tational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Lon...

    2021 doi

  77. [88]

    Quase: Sequence editing under quantifiable guidance

    Yi Liao et al. “Quase: Sequence editing under quantifiable guidance”. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing . 2018, pp. 3855–3864

    2018

  78. [89]

    Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems

    Wang Ling et al. “Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems”. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 2017, pp. 158– 167

    2017

  79. [90]

    Revision in Continuous Space: Fine-Grained Control of Text Style Transfer

    Dayiheng Liu et al. “Revision in Continuous Space: Fine-Grained Control of Text Style Transfer”. In: (2020)

    2020

  80. [91]

    Roberta: A robustly optimized bert pretraining approach

    Yinhan Liu et al. “Roberta: A robustly optimized bert pretraining approach”. In: arXiv preprint arXiv:1907.11692 (2019)

    2019 arXiv

  81. [92]

    A Novel Random Subspace Method for Online Writeprint Identification

    Zhi Liu et al. “A Novel Random Subspace Method for Online Writeprint Identification.” In: J. Comput. 7.12 (2012), pp. 2997–3004

    2012

  82. [93]

    Content preserving text gen- eration with attribute controls

    Lajanugen Logeswaran, Honglak Lee, and Samy Bengio. “Content preserving text gen- eration with attribute controls”. In:Advances in Neural Information Processing Systems. 2018, pp. 5103–5113

    2018

  83. [94]

    A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer

    Fuli Luo et al. “A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer”. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019. 2019

    2019

  84. [95]

    A dual reinforcement learning framework for unsupervised text style transfer

    Fuli Luo et al. “A dual reinforcement learning framework for unsupervised text style transfer”. In: Proceedings of the 28th International Joint Conference on Artificial Intel- ligence. AAAI Press. 2019, pp. 5116–5122. Bibliography 97

    2019

  85. [96]

    StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer

    Yiwei Lyu et al. “StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer”. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technolo- gies. Online: Association f...

    2021 doi

  86. [97]

    Politeness Transfer: A Tag and Generate Approach

    Aman Madaan et al. “Politeness Transfer: A Tag and Generate Approach”. In: Pro- ceedings of the 58th Annual Meeting of the Association for Computational Linguistics . Online: Association for Computational Linguistics, July 2020, pp. 1869–1881. DOI: 10 . 18653 / v1 / 2020 . acl...

    2020

  87. [98]

    PEFT: State-of-the-art Parameter-Efficient Fine-Tuning meth- ods

    Sourab Mangrulkar et al. PEFT: State-of-the-art Parameter-Efficient Fine-Tuning meth- ods. https://github.com/huggingface/peft. 2022

    2022

  88. [99]

    Transferring bert-like transformers’ knowledge for authorship verification

    Andrei Manolache et al. “Transferring bert-like transformers’ knowledge for authorship verification”. In: arXiv preprint arXiv:2112.05125 (2021)

    2021 arXiv

  89. [100]

    UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning

    Yuning Mao et al. “UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning”. In: ArXiv abs/2110.07577 (2021)

    2021 arXiv

  90. [101]

    A Computational Theory of Prose Style for Natural Language Generation

    David D. McDonald and James D. Pustejovsky. “A Computational Theory of Prose Style for Natural Language Generation”. In: Second Conference of the European Chap- ter of the Association for Computational Linguistics. Geneva, Switzerland: Association for Computational Linguistics...

    1985

  91. [102]

    Sequence to better sequence: con- tinuous revision of combinatorial structures

    Jonas Mueller, David Gifford, and Tommi Jaakkola. “Sequence to better sequence: con- tinuous revision of combinatorial structures”. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70. 2017, pp. 2536–2544

    2017

  92. [103]

    Crosslingual generalization through multitask finetuning

    Niklas Muennighoff et al. “Crosslingual generalization through multitask finetuning”. In: arXiv preprint arXiv:2211.01786 (2022)

    2022 arXiv

  93. [104]

    Surveying stylometry techniques and applications

    Tempestt Neal et al. “Surveying stylometry techniques and applications”. In: ACM Com- puting Surveys (CSuR) 50.6 (2017), pp. 1–36

    2017

  94. [105]

    Large-scale Hierarchical Alignment for Author Style Transfer

    Nikola I. Nikolov and Richard H. R. Hahnloser. “Large-scale Hierarchical Alignment for Author Style Transfer”. In: CoRR abs/1810.08237 (2018). arXiv: 1810.08237

    2018 arXiv

  95. [106]

    Improving LSTMs’ under-performance in authorship attribution for short texts

    Christian Oliva et al. “Improving LSTMs’ under-performance in authorship attribution for short texts”. In: Proceedings of the 2022 European Interdisciplinary Cybersecurity Conference. 2022, pp. 99–101

    2022

  96. [107]

    Representation learning with con- trastive predictive coding

    Aaron van den Oord, Yazhe Li, and Oriol Vinyals. “Representation learning with con- trastive predictive coding”. In: arXiv preprint arXiv:1807.03748 (2018)

    2018 arXiv

  97. [108]

    GPT-4 Technical Report

    OpenAI. “GPT-4 Technical Report”. In: CoRR abs/2303.08774 (2023)

    2023 arXiv

  98. [109]

    Introducing chatgpt

    OpenAI. Introducing chatgpt. https://openai.com/blog/chatgpt. 2022

    2022

  99. [110]

    Paraphrase Diversification Using Counterfactual Debiasing

    Sunghyun Park et al. “Paraphrase Diversification Using Counterfactual Debiasing”. In: Proceedings of the AAAI Conference on Artificial Intelligence. V ol. 33. 2019, pp. 6883– 6891

    2019

  100. [111]

    Are NLP Models really able to Solve Simple Math Word Problems?

    Arkil Patel, Satwik Bhattamishra, and Navin Goyal. “Are NLP Models really able to Solve Simple Math Word Problems?” In:Proceedings of NAACL. 2021, pp. 2080–2094. URL: https://aclanthology.org/2021.naacl-main.168. Bibliography 98

    2021

  101. [112]

    AdapterHub: A Framework for Adapting Transformers

    Jonas Pfeiffer et al. “AdapterHub: A Framework for Adapting Transformers”. In: Pro- ceedings of the 2020 Conference on Empirical Methods in Natural Language Process- ing: System Demonstrations. 2020, pp. 46–54

    2020

  102. [113]

    MAD-X: An Adapter-based Framework for Multi-task Cross- lingual Transfer

    Jonas Pfeiffer et al. “MAD-X: An Adapter-based Framework for Multi-task Cross- lingual Transfer”. In: Conference on Empirical Methods in Natural Language Process- ing. 2020

    2020

  103. [114]

    On variational bounds of mutual information

    Ben Poole et al. “On variational bounds of mutual information”. In: International Con- ference on Machine Learning. PMLR. 2019, pp. 5171–5180

    2019

  104. [115]

    Style Transfer Through Back-Translation

    Shrimai Prabhumoye et al. “Style Transfer Through Back-Translation”. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018, pp. 866–876

    2018

  105. [116]

    Is ChatGPT a general-purpose natural language processing task solver?

    Chengwei Qin et al. “Is ChatGPT a general-purpose natural language processing task solver?” In: arXiv preprint arXiv:2302.06476 (2023)

    2023 arXiv

  106. [117]

    Exploring Universal Intrinsic Task Subspace via Prompt Tuning

    Yujia Qin et al. “Exploring Universal Intrinsic Task Subspace via Prompt Tuning”. In: arXiv e-prints (2021), arXiv–2110

    2021

  107. [118]

    Personalized Machine Translation: Preserving Original Author Traits

    Ella Rabinovich et al. “Personalized Machine Translation: Preserving Original Author Traits”. In: Proceedings of the 15th Conference of the European Chapter of the Associ- ation for Computational Linguistics: Volume 1, Long Papers. Valencia, Spain: Associa- tion for Computatio...

    2017

  108. [119]

    Learning to generate reviews and discovering sentiment

    Alec Radford, Rafal Jozefowicz, and Ilya Sutskever. “Learning to generate reviews and discovering sentiment”. In: arXiv preprint arXiv:1704.01444 (2017)

    2017 arXiv

  109. [120]

    Language models are unsupervised multitask learners

    Alec Radford et al. “Language models are unsupervised multitask learners”. In: OpenAI Blog 1.8 (2019), p. 9

    2019

  110. [122]

    Exploring the limits of transfer learning with a unified text-to-text transformer

    Colin Raffel et al. “Exploring the limits of transfer learning with a unified text-to-text transformer”. In: The Journal of Machine Learning Research 21.1 (2020), pp. 5485– 5551

    2020

  111. [123]

    Dear Sir or Madam, May I Introduce the GY AFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer

    Sudha Rao and Joel Tetreault. “Dear Sir or Madam, May I Introduce the GY AFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer”. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Lang...

    2018

  112. [124]

    Sentence-bert: Sentence embeddings using siamese bert-networks

    Nils Reimers and Iryna Gurevych. “Sentence-bert: Sentence embeddings using siamese bert-networks”. In: arXiv preprint arXiv:1908.10084 (2019)

    2019 arXiv

  113. [125]

    Learning Universal Authorship Representations

    Rafael A. Rivera-Soto et al. “Learning Universal Authorship Representations”. In: Pro- ceedings of the 2021 Conference on Empirical Methods in Natural Language Process- ing. Online and Punta Cana, Dominican Republic: Association for Computational Lin- guistics, Nov. 2021, pp. ...

    2021 doi

  114. [126]

    Contrastive Learning with Hard Negative Samples

    Joshua David Robinson et al. “Contrastive Learning with Hard Negative Samples”. In: International Conference on Learning Representations . 2021. URL: https : / / openreview.net/forum?id=CR1XOQ0UTh-

    2021

  115. [127]

    Adversarial Decomposition of Text Representation

    Alexey Romanov et al. Adversarial Decomposition of Text Representation. 2019. arXiv: 1808.09042 [cs.CL]

    2019 arXiv

  116. [128]

    Solving general arithmetic word problems

    Subhro Roy and Dan Roth. “Solving general arithmetic word problems”. In: arXiv preprint arXiv:1608.01413 (2016)

    2016 arXiv

  117. [129]

    Character-level and multi-channel convolutional neural networks for large-scale authorship attribution

    Sebastian Ruder, Parsa Ghaffari, and John G Breslin. “Character-level and multi-channel convolutional neural networks for large-scale authorship attribution”. In:arXiv preprint arXiv:1609.06686 (2016)

    2016 arXiv

  118. [130]

    Siamese networks for large-scale author identifica- tion

    Chakaveh Saedi and Mark Dras. “Siamese networks for large-scale author identifica- tion”. In: Computer Speech & Language 70 (2021), p. 101241

    2021

  119. [131]

    Winogrande: An adversarial winograd schema challenge at scale

    Keisuke Sakaguchi et al. “Winogrande: An adversarial winograd schema challenge at scale”. In: Communications of the ACM 64.9 (2021), pp. 99–106

    2021

  120. [133]

    DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

    Victor Sanh et al. “DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter”. In: arXiv preprint arXiv:1910.01108 (2019)

    2019 arXiv

  121. [134]

    Socialiqa: Commonsense reasoning about social interactions

    Maarten Sap et al. “Socialiqa: Commonsense reasoning about social interactions”. In: arXiv preprint arXiv:1904.09728 (2019)

    2019 arXiv

  122. [135]

    Cross-topic authorship attribution: Will out-of-topic data help?

    Upendra Sapkota et al. “Cross-topic authorship attribution: Will out-of-topic data help?” In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers. 2014, pp. 1228–1237

    2014

  123. [136]

    Neural and Non-neural Approaches to Authorship Attribution

    Yunita Sari. “Neural and Non-neural Approaches to Authorship Attribution”. PhD the- sis. University of Sheffield, 2018

    2018

  124. [137]

    Authorship attribution of micro-messages

    Roy Schwartz et al. “Authorship attribution of micro-messages”. In: Proceedings of the 2013 Conference on empirical methods in natural language processing. 2013, pp. 1880– 1891

    2013

  125. [138]

    Improving Neural Machine Trans- lation Models with Monolingual Data

    Rico Sennrich, Barry Haddow, and Alexandra Birch. “Improving Neural Machine Trans- lation Models with Monolingual Data”. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016, pp. 86– 96

    2016

  126. [139]

    Authorship attribution with la- tent Dirichlet allocation

    Yanir Seroussi, Ingrid Zukerman, and Fabian Bohnert. “Authorship attribution with la- tent Dirichlet allocation”. In: Proceedings of the fifteenth conference on computational natural language learning. 2011, pp. 181–189

    2011

  127. [140]

    Authorship attribution with topic models

    Yanir Seroussi, Ingrid Zukerman, and Fabian Bohnert. “Authorship attribution with topic models”. In: Computational Linguistics 40.2 (2014), pp. 269–310

    2014

  128. [141]

    Semi-supervised Text Style Transfer: Cross Projection in La- tent Space

    Mingyue Shang et al. “Semi-supervised Text Style Transfer: Cross Projection in La- tent Space”. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). ...

    2019

  129. [142]

    Educating Text Autoencoders: Latent Representation Guidance via Denoising

    Tianxiao Shen et al. Educating Text Autoencoders: Latent Representation Guidance via Denoising. 2020. arXiv: 1905.12777 [cs.LG]

    2020 arXiv

  130. [143]

    Style transfer from non-parallel text by cross-alignment

    Tianxiao Shen et al. “Style transfer from non-parallel text by cross-alignment”. In: Ad- vances in neural information processing systems. 2017, pp. 6830–6841

    2017

  131. [144]

    HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace

    Yongliang Shen et al. “HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace”. In: CoRR abs/2303.17580 (2023)

    2023 arXiv

  132. [145]

    Convolutional neural networks for authorship attribution of short texts

    Prasha Shrestha et al. “Convolutional neural networks for authorship attribution of short texts”. In: Proceedings of the 15th conference of the European chapter of the association for computational linguistics: Volume 2, short papers. 2017, pp. 669–674

    2017

  133. [146]

    Mpnet: Masked and permuted pre-training for language understand- ing

    Kaitao Song et al. “Mpnet: Masked and permuted pre-training for language understand- ing”. In: Advances in Neural Information Processing Systems 33 (2020), pp. 16857– 16867

    2020

  134. [147]

    Authorship verification: a review of recent advances

    Efstathios Stamatatos. “Authorship verification: a review of recent advances”. In: Re- search in Computing Science 123 (2016), pp. 9–25

    2016

  135. [148]

    Plagiarism and authorship analysis: intro- duction to the special issue

    Efstathios Stamatatos and Moshe Koppel. “Plagiarism and authorship analysis: intro- duction to the special issue”. In: Language Resources and Evaluation 45 (2011), pp. 1– 4

    2011

  136. [149]

    “Transforming

    Akhilesh Sudhakar, Bhargav Upadhyay, and Arjun Maheswaran. ““Transforming” Delete, Retrieve, Generate Approach for Controlled Text Style Transfer”. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Confer...

    2019

  137. [150]

    Stylometric and neural features com- bined deep Bayesian classifier for authorship verification

    Yitao Sun, Svetlana Afanaseva, and Kailash Patil. “Stylometric and neural features com- bined deep Bayesian classifier for authorship verification”. In: Working Notes of CLEF (2023)

    2023

  138. [151]

    LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning

    Yi-Lin Sung, Jaemin Cho, and Mohit Bansal. “LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning”. In: ArXiv abs/2206.06522 (2022)

    2022 arXiv

  139. [152]

    Sequence to sequence learning with neu- ral networks

    Ilya Sutskever, Oriol Vinyals, and Quoc V Le. “Sequence to sequence learning with neu- ral networks”. In: Advances in neural information processing systems. 2014, pp. 3104– 3112

    2014

  140. [153]

    Policy gradient methods for reinforcement learning with func- tion approximation

    Richard S Sutton et al. “Policy gradient methods for reinforcement learning with func- tion approximation”. In: Advances in neural information processing systems 12 (1999)

    1999

  141. [154]

    Adapting language models for non-parallel author-stylized rewrit- ing

    Bakhtiyar Syed et al. “Adapting language models for non-parallel author-stylized rewrit- ing”. In: Proceedings of the AAAI Conference on Artificial Intelligence . V ol. 34. 05. 2020, pp. 9008–9015

    2020

  142. [155]

    Stanford Alpaca: An Instruction-following LLaMA model

    Rohan Taori et al. Stanford Alpaca: An Instruction-following LLaMA model . https: //github.com/tatsu-lab/stanford_alpaca. 2023

    2023

  143. [156]

    A needle in a haystack? Harnessing onomatopoeia and user-specific stylometrics for authorship attribution of micro-messages

    Antônio Theóphilo, Luís AM Pereira, and Anderson Rocha. “A needle in a haystack? Harnessing onomatopoeia and user-specific stylometrics for authorship attribution of micro-messages”. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing ...

    2019

  144. [157]

    Structured Content Preservation for Unsuper- vised Text Style Transfer

    Youzhi Tian, Zhiting Hu, and Zhou Yu. “Structured Content Preservation for Unsuper- vised Text Style Transfer”. In: CoRR abs/1810.06526 (2018). arXiv: 1810.06526

    2018 arXiv

  145. [158]

    Llama 2: Open foundation and fine-tuned chat models

    Hugo Touvron et al. “Llama 2: Open foundation and fine-tuned chat models”. In: arXiv preprint arXiv:2307.09288 (2023)

    2023 arXiv

  146. [160]

    Llama: Open and efficient foundation language models

    Hugo Touvron et al. “Llama: Open and efficient foundation language models”. In: arXiv preprint arXiv:2302.13971 (2023)

    2023 arXiv

  147. [161]

    On the state of the art in authorship attribution and authorship verification

    Jacob Tyo, Bhuwan Dhingra, and Zachary C Lipton. “On the state of the art in authorship attribution and authorship verification”. In: arXiv preprint arXiv:2209.06869 (2022)

    2022 arXiv

  148. [162]

    Heterogeneous-Graph Convolutional Network for Authorship Verification

    Andric Valdez-Valenzuela, Jorge Alfonso Martinez-Galicia, and Helena Gómez-Adorno. “Heterogeneous-Graph Convolutional Network for Authorship Verification”. In: (2023)

    2023

  149. [163]

    Visualizing data using t-SNE

    Laurens Van der Maaten and Geoffrey Hinton. “Visualizing data using t-SNE.” In: Jour- nal of machine learning research 9.11 (2008)

    2008

  150. [164]

    Attention is all you need

    Ashish Vaswani et al. “Attention is all you need”. In: Advances in neural information processing systems. 2017, pp. 5998–6008

    2017

  151. [165]

    Disentangled Representation Learning for Non-Parallel Text Style Transfer

    John Vineet et al. “Disentangled Representation Learning for Non-Parallel Text Style Transfer”. In: Proceedings of the 57th Annual Meeting of the Association for Computa- tional Linguistics. 2019, pp. 424–434

    2019

  152. [166]

    Pointer networks

    Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. “Pointer networks”. In: Advances in neural information processing systems. 2015, pp. 2692–2700

    2015

  153. [167]

    Spot: Better frozen model adaptation through soft prompt transfer

    Tu Vu et al. “Spot: Better frozen model adaptation through soft prompt transfer”. In: arXiv preprint arXiv:2110.07904 (2021)

    2021 arXiv

  154. [168]

    Can Authorship Representation Learning Capture Stylistic Fea- tures?

    Andrew Wang et al. “Can Authorship Representation Learning Capture Stylistic Fea- tures?” In: Transactions of the Association for Computational Linguistics 11 (2023), pp. 1416–1431

    2023

  155. [169]

    GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model

    Ben Wang and Aran Komatsuzaki. GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model. https://github.com/kingoflolz/mesh-transformer- jax. May 2021

    2021

  156. [170]

    CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language Understanding

    Dong Wang et al. CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language Understanding. 2021. arXiv: 2107.00440 [cs.CL]

    2021 arXiv

  157. [171]

    Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation

    Ke Wang, Hang Hua, and Xiaojun Wan. “Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation”. In: Advances in Neural Infor- mation Processing Systems. 2019, pp. 11034–11044

    2019

  158. [173]

    Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models

    Lei Wang et al. “Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models”. In: arXiv preprint arXiv:2305.04091 (2023)

    2023 arXiv

  159. [174]

    AdaMix: Mixture-of-Adapter for Parameter-efficient Tuning of Large Language Models

    Yaqing Wang et al. “AdaMix: Mixture-of-Adapter for Parameter-efficient Tuning of Large Language Models”. In: ArXiv abs/2205.12410 (2022). Bibliography 102

    2022 arXiv

  160. [175]

    Harnessing Pre-Trained Neural Networks with Rules for Formal- ity Style Transfer

    Yunli Wang et al. “Harnessing Pre-Trained Neural Networks with Rules for Formal- ity Style Transfer”. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJC...

    2019

  161. [176]

    Chain-of-thought prompting elicits reasoning in large language mod- els

    Jason Wei et al. “Chain-of-thought prompting elicits reasoning in large language mod- els”. In: Advances in neural information processing systems 35 (2022), pp. 24824– 24837

    2022

  162. [177]

    Simple statistical gradient-following algorithms for connectionist reinforcement learning

    Ronald J Williams. “Simple statistical gradient-following algorithms for connectionist reinforcement learning”. In: Machine learning 8.3-4 (1992), pp. 229–256

    1992

  163. [178]

    Transformers: State-of-the-Art Natural Language Processing

    Thomas Wolf et al. “Transformers: State-of-the-Art Natural Language Processing”. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Pro- cessing: System Demonstrations . Online: Association for Computational Linguistics, Oct. 2020, pp. 38–45. URL: ...

    2020

  164. [179]

    A Hierarchical Reinforced Sequence Operation Method for Unsuper- vised Text Style Transfer

    Chen Wu et al. “A Hierarchical Reinforced Sequence Operation Method for Unsuper- vised Text Style Transfer”. In: Proceedings of the 57th Annual Meeting of the Associa- tion for Computational Linguistics. 2019, pp. 4873–4883

    2019

  165. [180]

    Unsupervised feature learning via non-parametric instance discrimi- nation

    Zhirong Wu et al. “Unsupervised feature learning via non-parametric instance discrimi- nation”. In: Proceedings of the IEEE conference on computer vision and pattern recog- nition. 2018, pp. 3733–3742

    2018

  166. [181]

    CLEAR: Contrastive Learning for Sentence Representation

    Zhuofeng Wu et al. CLEAR: Contrastive Learning for Sentence Representation . 2020. arXiv: 2012.15466 [cs.CL]

    2020 arXiv

  167. [182]

    Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforce- ment Learning Approach

    Jingjing Xu et al. “Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforce- ment Learning Approach”. In: Proceedings of the 56th Annual Meeting of the Associa- tion for Computational Linguistics (Volume 1: Long Papers). 2018, pp. 979–988

    2018

  168. [183]

    On Variational Learning of Con- trollable Representations for Text without Supervision

    Peng Xu, Yanshuai Cao, and Jackie Chi Kit Cheung. “On Variational Learning of Con- trollable Representations for Text without Supervision”. In:CoRR abs/1905.11975 (2019). arXiv: 1905.11975

    2019 arXiv

  169. [184]

    On variational learning of con- trollable representations for text without supervision

    Peng Xu, Jackie Chi Kit Cheung, and Yanshuai Cao. “On variational learning of con- trollable representations for text without supervision”. In: International Conference on Machine Learning. PMLR. 2020, pp. 10534–10543

    2020

  170. [185]

    Formality Style Transfer with Hybrid Textual Annotations

    Ruochen Xu, Tao Ge, and Furu Wei. “Formality Style Transfer with Hybrid Textual Annotations”. In: CoRR abs/1903.06353 (2019). arXiv: 1903.06353

    2019 arXiv

  171. [186]

    Paraphrasing for style

    Wei Xu et al. “Paraphrasing for style”. In: Proceedings of COLING 2012. 2012, pp. 2899– 2914

    2012

  172. [187]

    Unsupervised text style transfer using language models as discrimi- nators

    Zichao Yang et al. “Unsupervised text style transfer using language models as discrimi- nators”. In: Advances in Neural Information Processing Systems. 2018, pp. 7287–7298

    2018

  173. [188]

    Utilizing non-parallel text for style transfer by making partial compar- isons

    Di Yin et al. “Utilizing non-parallel text for style transfer by making partial compar- isons”. In: Proceedings of the 28th International Joint Conference on Artificial Intelli- gence. AAAI Press. 2019, pp. 5379–5386

    2019

  174. [189]

    Improving Zero-shot Voice Style Transfer via Disentangled Represen- tation Learning

    Siyang Yuan et al. Improving Zero-shot Voice Style Transfer via Disentangled Represen- tation Learning. 2021. arXiv: 2103.09420 [eess.AS]. Bibliography 103

    2021 arXiv

  175. [190]

    ChatDoctor: A Medical Chat Model Fine-tuned on LLaMA Model using Medical Domain Knowledge

    Li Yunxiang et al. “ChatDoctor: A Medical Chat Model Fine-tuned on LLaMA Model using Medical Domain Knowledge”. In: arXiv preprint arXiv:2303.14070 (2023)

    2023 arXiv

  176. [191]

    Syntax Encoding with Application in Authorship Attribution

    Richong Zhang et al. “Syntax Encoding with Application in Authorship Attribution”. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels, Belgium: Association for Computational Linguistics, Oct. 2018, pp. 2742–2753. DOI: 10.18653/...

    2018 doi

  177. [192]

    Opt: Open pre-trained transformer language models

    Susan Zhang et al. “Opt: Open pre-trained transformer language models”. In: arXiv preprint arXiv:2205.01068 (2022)

    2022 arXiv

  178. [193]

    Bertscore: Evaluating text generation with bert

    Tianyi Zhang et al. “Bertscore: Evaluating text generation with bert”. In: arXiv preprint arXiv:1904.09675 (2019)

    2019 arXiv

  179. [194]

    Character-level convolutional networks for text classification

    Xiang Zhang, Junbo Zhao, and Yann LeCun. “Character-level convolutional networks for text classification”. In:Advances in neural information processing systems28 (2015)

    2015

  180. [195]

    SHAPED: Shared-Private Encoder-Decoder for Text Style Adaptation

    Ye Zhang, Nan Ding, and Radu Soricut. “SHAPED: Shared-Private Encoder-Decoder for Text Style Adaptation”. In: Proceedings of NAACL-HLT. 2018, pp. 1528–1538

    2018

  181. [196]

    Parallel Data Augmentation for Formality Style Trans- fer

    Yi Zhang, Tao Ge, and Xu Sun. “Parallel Data Augmentation for Formality Style Trans- fer”. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020

    2020

  182. [197]

    Learning Sentiment Memories for Sentiment Modification without Par- allel Data

    Yi Zhang et al. “Learning Sentiment Memories for Sentiment Modification without Par- allel Data”. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018, pp. 1103–1108

    2018

  183. [198]

    Style Transfer as Unsupervised Machine Translation

    Zhirui Zhang et al. “Style Transfer as Unsupervised Machine Translation”. In: arXiv (2018), arXiv–1808

    2018

  184. [199]

    Style Transfer as Unsupervised Machine Translation

    Zhirui Zhang et al. “Style Transfer as Unsupervised Machine Translation”. In: CoRR abs/1808.07894 (2018). arXiv: 1808.07894

    2018 arXiv

  185. [200]

    Adversarially regularized autoencoders

    Jake Zhao et al. “Adversarially regularized autoencoders”. In: 35th International Con- ference on Machine Learning, ICML 2018 . International Machine Learning Society (IMLS). 2018, pp. 9405–9420

    2018

  186. [201]

    Language Style Transfer from Sentences with Arbitrary Unknown Styles

    Yanpeng Zhao et al. “Language Style Transfer from Sentences with Arbitrary Unknown Styles”. In: CoRR abs/1808.04071 (2018). arXiv: 1808.04071

    2018 arXiv

  187. [202]

    A review on authorship attribution in text mining

    Wanwan Zheng and Mingzhe Jin. “A review on authorship attribution in text mining”. In: Wiley Interdisciplinary Reviews: Computational Statistics15.2 (2023), e1584

    2023

  188. [203]

    Exploring Contextual Word-level Style Relevance for Unsupervised Style Transfer

    Chulun Zhou et al. “Exploring Contextual Word-level Style Relevance for Unsupervised Style Transfer”. In:Proceedings of the 58th Annual Meeting of the Association for Com- putational Linguistics. 2020

    2020

  189. [204]

    Unpaired Image-to-Image Translation using Cycle-Consistent Ad- versarial Networks

    Jun-Yan Zhu et al. Unpaired Image-to-Image Translation using Cycle-Consistent Ad- versarial Networks. 2020. arXiv: 1703.10593 [cs.CV]

    2020 arXiv

  190. [2017]

    URL: https://openreview.net/forum?id=rkE3y85ee

  191. [2022]

    2022, pp

    Abu Dhabi, United Arab Emirates: Association for Computational Linguistics, Dec. 2022, pp. 2184–2190.URL: https://aclanthology.org/2022.findings- emnlp.160

    2022

  192. [3479]

    URL: https://proceedings.mlr.press/v139/fu21a.html

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.