Pith. sign in

REVIEW 4 major objections 6 minor 23 references

Small BERT/RoBERTa models get their biggest, most reliable boost from GPT-generated training examples, while extra domain pre-training helps BERT but hurts RoBERTa.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

T0 review · deepseek-v4-flash

2026-08-05 05:42 UTC pith:2MBFEQRJ

load-bearing objection A useful single-run empirical comparison; the augmentation claim is undercut by label-distribution shift and roberta-large's consistent degradation. the 4 major comments →

arxiv 2509.04982 v1 pith:2MBFEQRJ submitted 2025-09-05 cs.CL cs.IRcs.LG

Optimizing Small Transformer-Based Language Models for Multi-Label Sentiment Analysis in Short Texts

Julius Neumann , Robert Lange , Yuni Susanti , Michael F\"arber This is my paper
classification cs.CL cs.IRcs.LG
keywords multi-label sentiment analysisshort text classificationgenerative data augmentationBERTRoBERTacontinued pre-trainingclassification headsSemEval 2025 Task 11
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

This paper asks whether three cheap interventions can make small BERT and RoBERTa models (under 1 billion parameters) better at tagging short texts with multiple emotions, using a 2,768-sample SemEval 2025 Task 11 corpus. The central finding is that generative data augmentation—fine-tuning GPT-4o-mini to produce new labeled short texts and adding them to the training set—improves F1 and accuracy for most models, with the fully augmented set giving the best results. Continued masked-language-model pre-training on the same domain helps BERT-base and BERT-large slightly, but consistently hurts both RoBERTa variants, which the authors attribute to RoBERTa's already-extensive pre-training and rapid overfitting on the small corpus. Reworking the classification head changes scores only marginally, though a projected-attention head matches a simple fully-connected head with far fewer trainable parameters. The best model also outperformed three human annotators on the development set, and SHAP analysis shows the model relies on transparent cues such as 'police' for fear.

Core claim

On a small, imbalanced, multi-label emotion dataset, adding a moderate-to-large amount of GPT-4o-mini-generated training examples is the most reliable performance lever: across BERT-base, BERT-large, and RoBERTa-base, the fully augmented training set yields the highest F1 scores, while RoBERTa-large degrades. Continued domain-specific pre-training only helps BERT models; for RoBERTa it reduces accuracy and F1, consistent with the interpretation that further MLM training on 2,768 short texts overfits quickly. Classification-head architecture is a minor factor: larger fully-connected heads do not beat the simple 768-wide head, and a small projected-attention head matches it with far fewer para

What carries the argument

The central mechanism is Generative Data Augmentation (GDA): a fine-tuned GPT-4o-mini generates 11,684 synthetic short texts with JSON label arrays, added to the original 2,768 texts at 33%, 66%, and 100% augmentation rates. The paper compares models fine-tuned with and without continued masked-language-model pre-training, and with fully-connected versus projected-attention classification heads, using accuracy, F1, macro F1, and Cohen's kappa on the predefined Codabench split.

Load-bearing premise

The paper's main claim assumes the GPT-generated examples are as good as real short texts; if they simply shifted the mix of emotions (Fear rose from 58% to 65%, Anger from 12% to 19%), the measured gains may be a rebalancing effect, not a data-quality effect.

What would settle it

Take the same SemEval 2025 Task 11 split and compare three equal-size training sets: original texts, original texts resampled to match the augmented label distribution, and original texts plus GPT-4o-mini examples. If the augmentation set does not beat the resampled set on a held-out real test set, the gain is a label-balancing artifact rather than a language-quality effect. Additionally, if continued pre-training with a different seed or smaller learning rate reverses the RoBERTa degradation, the 'pre-training adds noise' conclusion would be shown to be sensitivity-dependent.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

Share X Bluesky LinkedIn Reddit HN

If this is right

  • Practitioners with small short-text datasets can lift F1 by generating synthetic labeled examples, even without elaborate filtering.
  • Continued domain-specific MLM pre-training should be applied selectively: BERT-style models may benefit, while RoBERTa-style models are likely to degrade.
  • Classification-head complexity is not worth much; simpler heads or projected-attention heads are preferable when parameters and compute are constrained.
  • On this subjective task, model scores should be interpreted against human agreement ceilings, since the best model (F1 0.77) exceeded three human annotators (F1 0.55–0.61).
  • Because the fully augmented set still helped most models, the paper suggests its dataset-size threshold has not been reached, so further expansion may yield additional gains.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The generated data changed the label mix substantially (Fear from 58.2% to 65.0%, Anger from 12.0% to 19.0%), so part of the gain may come from rebalancing underrepresented classes rather than from richer language; a controlled comparison against resampling or reweighting the original data would separate these effects.
  • The authors did not filter generated examples; adding a confidence- or consistency-based filter might let smaller augmentation rates match or exceed the full 100% set with less noise.
  • Because RoBERTa-large already led on the original set and dropped with augmentation, the optimal recipe likely depends on model maturity; a testable extension is to measure augmentation benefit against the base model's pre-training corpus size.
  • The human-evaluation comparison used three non-native speakers on one dev subset, so 'model outperforms humans' should not be generalized; a native-speaker annotation study would tighten that claim.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper investigates three optimization strategies for small Transformer-based models (BERT and RoBERTa variants) on multi-label sentiment classification of short texts from SemEval 2025 Task 11: continued domain-specific pre-training, generative data augmentation with a fine-tuned GPT-4o-mini model, and classification head variations. The main claims are that moderate generative data augmentation improves classification performance, continued pre-training on augmented data can introduce noise rather than improve accuracy, and classification head modifications yield marginal benefits. The evaluation uses a single predefined split, four base models, multiple augmentation fractions, SHAP explanations, and a small human annotation study.

Significance. If the claims were robust, the paper would provide practical guidance for practitioners working with small models on resource-constrained short-text sentiment tasks, an area where systematic comparisons are relatively scarce. The authors also release code and data, which is commendable. However, the central empirical claims are not currently established: Table 3 contains multiple configurations where augmentation lowers F1, the augmented label distribution differs substantially from the original, and all conclusions rest on single runs without error bars or significance testing. The human evaluation, while interesting, is too small and uncontrolled to support the 'outperforms human annotators' claim. The paper has useful material, but the headline conclusions need substantial re-analysis or reframing.

major comments (4)
  1. [Section 4.1, Table 3] The abstract and Section 4.1 claim that 'data augmentation improves classification performance,' but Table 3 contradicts this for many configurations. For example, roberta-large without pre-training drops from F1 0.77 on the original dataset to 0.719 at 0.33 augmentation; bert-base with pre-training drops from 0.694 to 0.687; roberta-base with pre-training drops from 0.707 to 0.678. The positive statement appears to rely on pooling across models and configurations, with no error bars or significance tests. Please report per-configuration comparisons, and either temper the claim or provide statistical support (e.g., multiple seeds and confidence intervals).
  2. [Section 3.1.2, Tables 1 and 2] The augmentation effect is confounded with label-distribution shift. The text states that the generator was fine-tuned 'to match the label distribution of the dataset under test,' but comparing Table 1 and Table 2 shows large shifts: Anger 12.0% to 19.0%, Fear 58.2% to 65.0%, Sadness 31.7% to 41.8%, Surprise 30.3% to 39.7%. Thus any F1 improvement from augmentation could stem from rebalancing toward rare labels rather than from the quality or diversity of synthetic texts. This undermines the central claim. A control experiment with class-balanced subsampling, class weights, or per-class metrics is needed to separate these effects.
  3. [Section 3.3 and all tables] All experiments use a single predefined split and a single run per configuration. Given that many reported differences are around 0.01 F1 (e.g., Table 3, bert-base Yes: 0.694 vs 0.687 at 0.33 augmentation), the results are not distinguishable from random variation. Please provide multiple random seeds, standard deviations, or a paired significance test. Without this, claims such as 'moderate generative data augmentation improves' and 'modifications to the classification head yield minimal gains' are not supported by the evidence presented.
  4. [Section 4.3, Table 5] The claim that the model 'significantly outperforms human annotators' is not justified. The comparison uses only three non-native English speakers on a subset of the development set, with no statistical test, no details of the selection of the subset, and no baseline comparison for human annotation variance. The Cohen's Kappa values in Table 6 are informative, but they measure agreement between human annotators, not human--model agreement. Please either present a rigorous human--model comparison or remove the 'significantly outperforms' wording.
minor comments (6)
  1. [Section 1] The definition of 'small' as 'fewer than 1 billion parameters' is unusually broad; BERT-large and RoBERTa-large are around 300-350M parameters. Please clarify the intended notion of 'small' and consider a more precise threshold or naming.
  2. [Section 3.3, Table 3] The augmentation fractions '0.33 Augmentation', etc., are not fully specified. State clearly whether these are fractions of the 11,684 generated examples added to the original 2,768 training instances, and give the resulting training-set sizes.
  3. [Figure 1 and Figure 2] The labels 'chcl' and 'hdd' in the figures are unclear; please define all abbreviations in the captions or in the main text.
  4. [References] There are formatting issues in the reference list, e.g., 'V ol-3853' in reference [13] and the unusual title of reference [18]. Please proofread and format consistently.
  5. [Section 4.1] The sentence 'improving all models except roberta-large' is too strong; Table 3 shows many configurations with decreased F1 for other models as well. Please phrase the conclusion in line with the per-model evidence.
  6. [Section 5.1, Appendix] The 'gt' artifact explanation is useful, but consider moving it to the main text or a footnote, since the SHAP interpretation in Section 4.2 may otherwise be misread.

Circularity Check

0 steps flagged

No significant circularity: central claims rest on held-out evaluation, not on self-citation or fitted inputs.

full rationale

This paper is an empirical evaluation study. Its three central claims — that generative data augmentation improves performance, that continued pre-training can add noise on RoBERTa, and that classification-head changes give marginal gains — are supported by fine-tuning and evaluation on the fixed SemEval 2025 Task 11 train/validation/test split (Tables 3 and 4). No parameter is fitted to a subset of data and then reported as a prediction of a closely related quantity; no quantity is defined in terms of the conclusion; and no uniqueness or existence argument is imported from prior work to force a choice. The self-citations that do occur are not load-bearing: the definition of 'small' as fewer than 1 billion parameters follows [17], and GDA is said to have been explored in [13], but these only contextualize the experimental setup and do not establish any result. The possible label-distribution shift between Tables 1 and 2 is a potential confound for the augmentation conclusion, but it is an experimental-validity concern, not a circularity: the outcome is measured against held-out gold labels rather than being assumed by construction. Accordingly, no circular step can be quoted or exhibited, and the appropriate finding is no significant circularity.

Axiom & Free-Parameter Ledger

5 free parameters · 4 axioms · 0 invented entities

The central evidence is empirical. The main free choices are hyperparameters and augmentation levels; the main unstated premises are that GPT-4o-mini generated samples are clean and domain-consistent, that a single split is sufficient, and that HuggingFace checkpoints are valid bases. No new entities are introduced.

free parameters (5)
  • augmentation_fraction = 0.33, 0.66, 1.0 (11,684 generated examples total)
    The three levels are the independent variable being tested. The paper chose the total number based on compute (Section 3.1.2), and results differ by level.
  • learning_rate = 2e-5
    Used for continued pretraining and fine-tuning (Sections 3.1.1 and 3.3); chosen by hand, not optimized.
  • epochs = pretraining 50, fine-tuning 30
    Fixed for all runs (Sections 3.1.1 and 3.3); no early stopping or tuning across configurations.
  • head_hyperparameters = l=1/c=768; l=2,4; projected attention d=128, k=1,2
    Architecture variants tested in Table 4; sizes are choices, not fitted.
  • gpt4o_mini_fine_tuning_epochs = 3
    Fine-tuning of the generator on a partitioned training set (Section 3.1.2); no ablation of this step.
axioms (4)
  • domain assumption HuggingFace pretrained checkpoints are valid starting points for the task.
    All models are initialized from published checkpoints (Section 3.3); no verification of checkpoint version or domain match.
  • domain assumption Synthetic examples are domain-consistent and comparable in quality to original texts.
    Stated in Section 3.1.2; not independently measured, and label distribution changes by up to 7 percentage points (Tables 1 and 2).
  • domain assumption The single predefined train/validation/test split captures stable model ordering.
    Section 3.3 uses one split; no repeated seeds or cross-validation, so variance is unknown.
  • domain assumption Masked language modeling on text fields is appropriate continued pretraining for sentiment.
    Section 3.1.1 follows Sun et al. [15]; no task-specific objective is tested.

pith-pipeline@v1.4.0-alltime-deepseek-medium · 9292 in / 12452 out tokens · 126632 ms · 2026-08-05T05:42:49.645899+00:00 · methodology

0 comments
Cite this review

Pith. "Pith review of Optimizing Small Transformer-Based Language Models for Multi-Label Sentiment Analysis in Short Texts." pith.science (2026). https://pith.science/paper/2MBFEQRJ

@misc{pith2026250904982,
  author       = {Pith},
  title        = {Pith review of: Optimizing Small Transformer-Based Language Models for Multi-Label Sentiment Analysis in Short Texts},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2MBFEQRJ}},
  note         = {Machine review of arXiv:2509.04982}
}
Share X Bluesky LinkedIn Reddit HN
read the original abstract

Sentiment classification in short text datasets faces significant challenges such as class imbalance, limited training samples, and the inherent subjectivity of sentiment labels -- issues that are further intensified by the limited context in short texts. These factors make it difficult to resolve ambiguity and exacerbate data sparsity, hindering effective learning. In this paper, we evaluate the effectiveness of small Transformer-based models (i.e., BERT and RoBERTa, with fewer than 1 billion parameters) for multi-label sentiment classification, with a particular focus on short-text settings. Specifically, we evaluated three key factors influencing model performance: (1) continued domain-specific pre-training, (2) data augmentation using automatically generated examples, specifically generative data augmentation, and (3) architectural variations of the classification head. Our experiment results show that data augmentation improves classification performance, while continued pre-training on augmented datasets can introduce noise rather than boost accuracy. Furthermore, we confirm that modifications to the classification head yield only marginal benefits. These findings provide practical guidance for optimizing BERT-based models in resource-constrained settings and refining strategies for sentiment classification in short-text datasets.

Figures

Figures reproduced from arXiv: 2509.04982 by Julius Neumann, Michael F\"arber, Robert Lange, Yuni Susanti.

Figure 1
Figure 1. Figure 1: Illustration of the fully-connected layer classification head. For each generated example, we provided the same prompt along with an output template in a JSON schema, which included a text field and an array of assigned sentiment labels. This procedure was repeated until a total of 11,684 augmented examples were generated, a number chosen to align with our available computational resources. Since the gener… view at source ↗
Figure 3
Figure 3. Figure 3: F1 Scores of the four base models with continued pre-training and fine-tuning on the original dataset [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Training loss of the four base models during continued pre-training on the original dataset. bert-base even outperforming bert-large in one instance. Conversely, the roberta-large model consistently outperforms roberta-base and is generally the best-performing model overall. Overall, although larger models, such as bert-large and roberta-large, generally perform better, smaller models can be improved effec… view at source ↗
Figure 5
Figure 5. Figure 5: Evaluation loss of the four base models during continued pre-training on the original dataset [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Average F1 Scores and accuracy across different data augmentation rates. prone to overfitting and a subsequent loss of generalization. Impact of Generative Data Augmentation. The evaluation results of the model for different data augmentation rates are shown in Fig￾ure 6 and [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: SHAP graph for “Fear” emotion class. 4.2 Explainability Analysis Results [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Confusion matrix of the model. References [1] S. Alaparthi and M. Mishra. Bidirectional encoder representations from transformers (bert): A sentiment analysis odyssey. ArXiv, abs/2007.01127, 2020. URL https://api.semanticscholar.org/CorpusID: 220302509. [2] S. V. Balkus and D. Yan. Improving short text classification with augmented data using gpt-3. Natural Language Engineering, 30(5): 943–972, 2024. doi: … view at source ↗
Figure 9
Figure 9. Figure 9: SHAP graph for “Anger” emotion class Joy [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗
Figure 11
Figure 11. Figure 11: SHAP graph for “Surprise” emotion class [PITH_FULL_IMAGE:figures/full_fig_p008_11.png] view at source ↗
Figure 10
Figure 10. Figure 10: SHAP graph for “Joy” emotion class 2 Here, gt is an artifact from scraped data (representing the “greater than” symbol in HTML encoding), likely introduced during web scraping. Its presence is spurious and should be disregarded in the interpretation. Surprise [PITH_FULL_IMAGE:figures/full_fig_p008_10.png] view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

23 extracted references · 13 canonical work pages · 4 internal anchors

  1. [1]

    Alaparthi and M

    S. Alaparthi and M. Mishra. Bidirectional encoder representations from transformers (bert): A sentiment analysis odyssey.ArXiv, abs/2007.01127, 2020. URL https://api.semanticscholar.org/CorpusID: 220302509

    arXiv 2007

  2. [2]

    S. V . Balkus and D. Yan. Improving short text classification with augmented data using gpt-3.Natural Language Engineering, 30(5): 943–972, 2024. doi: 10.1017/S1351324923000438

    doi:10.1017/s1351324923000438 2024

  3. [3]

    Bridging the gap in text-based emotion detection - se- meval 2025 task 11 - track a

    Codabench. Bridging the gap in text-based emotion detection - se- meval 2025 task 11 - track a. Codabench, 2024-25. URL https: //www.codabench.org/competitions/3863. Accessed: 2025-01-13

    work page 2025

  4. [4]

    Devlin, M.-W

    J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding, 2019. URL https://arxiv.org/abs/1810.04805

    Pith/arXiv arXiv 2019

  5. [5]

    On the Domain Adaptation and Generalization of Pretrained Language Models: A Survey

    X. Guo and H. Yu. On the domain adaptation and generalization of pretrained language models: A survey, 2022. URL https://arxiv.org/abs/ 2211.03154

    work page internal anchor Pith review Pith/arXiv arXiv 2022

  6. [6]

    Contextual Sentence Classification: Detecting Sustainability Initiatives in Company Reports

    D. Hirlea, C. Bryant, and M. Rei. Contextual sentence classifica- tion: Detecting sustainability initiatives in company reports.ArXiv, abs/2110.03727, 2021. URL https://api.semanticscholar.org/CorpusID: 238531297

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  7. [7]

    Y . Ji, Y . Deng, Y . Gong, Y . Peng, Q. Niu, L. Zhang, B. Ma, and X. Li. Exploring the impact of instruction data scaling on large language mod- els: An empirical study on real-world use cases.ArXiv, abs/2303.14742,

    Pith/arXiv arXiv

  8. [8]

    Y . Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V . Stoyanov. Roberta: A robustly optimized bert pretraining approach, 2019. URL https://arxiv.org/abs/1907.11692

    Pith/arXiv arXiv 2019

  9. [9]

    S. M. Lundberg and S.-I. Lee. Shap. In I. Guyon, U. V . Luxburg, S. Ben- gio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 30. Cur- ran Associates, Inc., 2017. URL https://proceedings.neurips.cc/paper_ files/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf

    work page 2017

  10. [10]

    Mehrafarin, S

    H. Mehrafarin, S. Rajaee, and M. T. Pilehvar. On the importance of data size in probing fine-tuned models. InFindings, 2022. URL https: //api.semanticscholar.org/CorpusID:247594470

    work page 2022

  11. [11]

    T. H. Nguyen, H. H. Nguyen, Z. Ahmadi, T.-A. Hoang, and T.-N. Doan. On the impact of dataset size:a twitter classification case study. InIEEE/WIC/ACM International Conference on Web Intelli- gence and Intelligent Agent Technology, WI-IAT ’21, page 210–217, New York, NY , USA, 2022. Association for Computing Machinery. ISBN 9781450391153. doi: 10.1145/3486...

    arXiv 2022

  12. [12]

    Y . Qu, D. Shen, Y . Shen, S. Sajeev, J. Han, and W. Chen. Coda: Contrast-enhanced and diversity-promoting data augmentation for nat- ural language understanding, 2020. URL https://arxiv.org/abs/2010. 08670

    work page 2020

  13. [13]

    Rogulsky, N

    S. Rogulsky, N. Popovic, and M. Färber. The effects of hallucinations in synthetic training data for relation extraction. InJoint proceedings of the 2nd workshop on Knowledge Base Construction from Pre-Trained Language Models (KBC-LM 2024) and the 3rd challenge on Language Models for Knowledge Base Construction (LM-KBC 2024) co-located with the 23nd Inter...

    work page 2024

  14. [14]

    A. C. Stickland and I. Murray. Bert and pals: Projected attention layers for efficient adaptation in multi-task learning, 2019. URL https://arxiv. org/abs/1902.02671

    Pith/arXiv arXiv 2019

  15. [15]

    C. Sun, X. Qiu, Y . Xu, and X. Huang. How to fine-tune bert for text classification?, 2020. URL https://arxiv.org/abs/1905.05583

    Pith/arXiv arXiv 2020

  16. [16]

    Y . Susanti. Data augmentation techniques for process extraction from scientific publications, 2024. URL https://arxiv.org/abs/2405.14594

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  17. [17]

    Susanti and M

    Y . Susanti and M. Färber. Knowledge graph structure as prompt: Im- proving small language models capabilities for knowledge-based causal discovery. InThe Semantic Web – ISWC 2024: 23rd International Se- mantic Web Conference, Baltimore, MD, USA, November 11–15, 2024, Proceedings, Part I, page 87–106, Berlin, Heidelberg, 2024. Springer- Verlag. ISBN 978-3...

    work page doi:10.1007/978-3-031-77844-5_ 2024

  18. [18]

    P. Verma. Attention is all you need? good embeddings with statis- tics are enough:large scale audio understanding without transformers/ convolutions/ berts/ mixers/ attention/ rnns or .... 2021. URL https: //api.semanticscholar.org/CorpusID:238419336

    work page 2021

  19. [19]

    URL https://doi.org/10.1007/978-3-031-77844-5_5

    work page doi:10.1007/978-3-031-77844-5_5

  20. [20]

    C. R. Wolfe and K. T. Lundgaard. Exceeding the limits of visual- linguistic multi-task learning.ArXiv, abs/2107.13054, 2021. URL https://api.semanticscholar.org/CorpusID:236469329

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  21. [21]

    T. Wolf, L. Debut, V . Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cis- tac, T. Rault, R. Louf, M. Funtowicz, and J. Brew. Huggingface’s transformers: State-of-the-art natural language processing.CoRR, abs/1910.03771, 2019. URL http://arxiv.org/abs/1910.03771

    Pith/arXiv arXiv 1910

  22. [23]

    Anger” class,insultingandfuriousare a strong posi- tive predictor, whileailmentsandlaughedcontribute minimally. In the “Joy

    Q. Xie, Z. Dai, E. Hovy, M.-T. Luong, and Q. V . Le. Unsupervised data augmentation for consistency training, 2020. URL https://arxiv.org/abs/ 1904.12848. 5.1 Appendix The following shows the class-specific influence graph for each sen- timent class produced by SHAP analysis. As explained in §4.2, these visualizations reveal the key features that influenc...

    Pith/arXiv arXiv 2020

  23. [2023]

    URL https://api.semanticscholar.org/CorpusID:257766844

    work page