Pith. sign in

REVIEW 4 major objections 5 minor 43 references

Song Form-aware Full-Song Text-to-Lyrics Generation with Multi-Level Granularity Syllable Count Control

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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

Pith's one-line read A language-model decoder conditioned on special syllable and song-form tokens can generate complete lyrics with controllable syllable counts at word, phrase, line, and paragraph levels.

desk verdict The paper's multi-level syllable control is genuinely strong at word/line/phrase level, but the paragraph-level claim is contradicted by its own Table 2a, so the abstract overreaches and the paper needs a forthright revision. read the letter →

arxiv 2411.13100 v3 pith:DIRLAYTP submitted 2024-11-20 cs.CL cs.AI

Yunkee Chae , Eunsik Shin , Suntae Hwang , Seungryeol Paik , Kyogu Lee This is my paper · ORCID
classification cs.CLcs.AI
keywords lyricsgenerationsyllablecountcontrolsongformtext-to-lyricsinfillingconditionaltextspecialtokenssemanticembedding
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

The paper sets out to show that the rhythmic core of lyrics—syllable counts at every level from a single word to a whole verse—can be controlled by a single language-model decoder, as long as the requested structure is handed to it as explicit tokens. It builds a full-song generation framework that takes an arbitrary text prompt, a song form (verse, chorus, bridge, and similar), and syllable-count targets for words, phrases, lines, and paragraphs, and produces complete lyrics under those constraints. This matters because fluent general-purpose language models count syllables poorly, and most lyrics systems either work line by line or require melody-audio paired data that is scarce. The paper reports syllable count distance of 0.003 and syllable error rate of 5.025% in its best generation setting, far below the large-language-model baselines it compares against, with comparable semantic coherence.

What carries the argument

The machinery is a structured token vocabulary: song-form tokens (<VERSE>, <CHORUS>, etc.), syllable-count tokens (<SYL:s>) that state a budget, and generation/infilling directive tokens (<GEN *>, <INF *>) with matching end tokens at each granularity. These tokens turn a lyrical plan into a sequence the decoder is trained to follow; the model is not left to infer syllable counts from sub-word tokenization. Training plans are synthesized by parsing lyrics into hierarchical trees (paragraphs, lines, phrases, word leaves) and traversing them in pre-order, and the semantic embedding of the input text is injected into the initial token embedding. During infilling, masked subtrees are marked with special tokens so the model can repair or replace a word, phrase, line, or paragraph using both past and future context.

What would settle it

Compare the automated syllable-counting library used in the paper with hand or dictionary syllable counts on a random sample of the test vocabulary; if disagreement is common, the model's near-zero syllable distance may be an artifact of the estimator rather than genuine rhythmic control.

Watch

Extended reading notes

Core claim

The central claim is that precise multi-level syllable control and song-form awareness can both be engineered into a lyrics decoder through a token interface rather than through explicit musical audio or rule-based post-processing. The model is trained on real lyrics converted into hierarchical generation plans; each plan tells it which song-form section to write, the exact syllable budget of the section, and the granularity of each segment. Semantic embeddings of the input text stand in for paired text-lyrics data, so any text prompt can steer content. In evaluation, the best model (back-conditioned, trained from scratch) achieves full-level syllable count distance 0.003 and syllable count error rate 5.025%, versus 0.194 and 79.828% for a large general-purpose language model, and an infilling variant preserves semantic similarity while matching syllable constraints.

Load-bearing premise

The reported syllable accuracy is only as trustworthy as the off-the-shelf syllable-counting library used to build the training plans and to score the outputs, and the paper does not independently verify those counts against a dictionary or human annotators.

Editorial extensions

If this is right

  • A songwriter can specify a song form and exact syllable counts per paragraph, line, phrase, or word and receive complete lyrics that meet those counts, without needing melody-audio paired training data.
  • The infilling mode can revise an existing lyric locally—replacing a word, phrase, line, or paragraph—while keeping the rest of the song and the syllable budget intact.
  • Training from scratch with the special-token structure is what drives syllable accuracy; pretrained weights help language quality but hurt count control, so the two objectives trade off in this design.
  • Song-form conditioning makes generated songs internally consistent: the paper finds that paragraphs of the same form (verse versus verse) are semantically and structurally closer to each other than to paragraphs of other forms.

Reading between the lines

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

  • A direct extension would be to test the same token-conditioning idea on other constrained generation tasks, such as metered poetry, subtitles with character limits, or spoken lines with exact pause counts.
  • The reported syllable accuracy has not been checked against human perception; an independent human-annotation study of syllable counts on generated lyrics would show whether the near-zero error rates reflect true rhythmic fit or the quirks of the automated estimator used to build and score the data.
  • Because training plans and evaluation metrics are derived from the same corpus and the same syllable counter, the model may be partly memorizing the estimator's behavior; training on plans from a second syllable counter or an annotated dataset would separate genuine control from estimator alignment.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper proposes a GPT-2-based framework for full-song lyrics generation and infilling, conditioned on semantic embeddings, song-form tokens, and multi-granularity syllable count tokens at the word, phrase, line, and paragraph levels. The authors train on a processed Genius Song Lyrics dataset, synthesize hierarchical generation plans via subtree masking, and evaluate with SCD, SCErr, PPL, BERT-S, and NLD. The best generation model, Back-S, is reported to achieve Full SCD 0.003 and SCErr 5.025%, well below ChatGPT-3.5/4 baselines, and the best infilling model, ILM-S, reports Full SCD 0.006 and SCErr 5.479% with BERT-S 0.856. Ablation studies show that specialized masking tokens and song-form tokens improve performance.

Significance. If the reported numbers are taken at face value, the paper makes a useful contribution: it is one of the first attempts to demonstrate syllable-count control at word, phrase, and line granularity in full-song generation, and the infilling formulation and song-form-consistency evaluation are novel. The controlled comparisons against ChatGPT-3.5/4, the use of Wilcoxon signed-rank tests across multiple model variants, and the ablation study are strengths. However, the significance is conditional on unresolved issues: SCErr is never formally defined, the abstract's paragraph-level control claim is not supported by the paragraph-level numbers in Table 2a, and the entire evaluation relies on a single syllable-counting library without independent validation. These issues must be addressed before the claimed multi-level syllable control can be accepted.

major comments (4)
  1. [Section 4.4, Eq. (1), Tables 1-3] SCErr is described only verbally as 'measures how frequently the model generates text with incorrect syllable counts', but no formula is given. The unit of analysis is ambiguous: is it the fraction of evaluated segments (word, phrase, line, paragraph, or full song) with any mismatch, or a pooled count of mismatched syllables? Because of this ambiguity, the relationship between Full SCErr 5.025% and Paragraph SCErr 68.777% for Back-S in Table 2a is uninterpretable. This is load-bearing because SCErr is the headline metric in Table 1 and the basis for the multi-level control claims. Please provide the exact definition, the aggregation rule across levels, and error distributions (e.g., percentages of segments off by 1, 2, or more syllables).
  2. [Table 2a, Abstract, Section 5.2] Back-S, presented as the best generation model, has paragraph-level SCErr 68.777%, meaning that under the natural reading of the verbal definition, roughly two-thirds of generated paragraphs do not exactly match the specified syllable counts, while the Full SCErr is only 5.025%. The Full aggregate can hide consistent per-paragraph errors through cancellation of positive and negative differences. The paper does not acknowledge or discuss this discrepancy; Section 5.2 reports overall syllable accuracy without mentioning the paragraph-level failure rate. Since the Abstract claims the framework 'enables multi-level syllable control at the word, phrase, line, and paragraph levels', this claim is not supported as stated. Please report exact-match rates and error statistics at each level, discuss the limitation, or revise the claim.
  3. [Sections 4.1 and 4.4] All syllable counts in both the training conditions and the evaluation metrics are computed with the same Syllables library [27], with no validation against a reference dictionary or human annotation. If this estimator is systematically wrong for the vocabulary of song lyrics (e.g., contractions, informal spellings, or word-stress ambiguity), then both the conditioning plans and the reported SCD/SCErr values are biased in the same direction, making the absolute accuracy numbers unreliable as evidence of true rhythmic fit. Please validate the estimator against an independent standard such as CMU Pronouncing Dictionary or manually annotated samples, report its per-word accuracy, and discuss how residual errors would affect the reported SCD/SCErr values.
  4. [Section 4.2, Eq. (1)] The SCD formula in Eq. (1) divides by \hat{s}_i, which is undefined if the generated unit has zero syllables; such cases may be rare but are not impossible in lyrics with interjections or incomplete tokens. Please either clip syllable counts to a minimum of 1 or specify how zero-count generated segments are handled in the metric.
minor comments (5)
  1. [Section 4.2, 4.3] The pre-order traversal selection probability p is introduced in Section 4.2 without a value and is set only later in Section 4.3; please state the value where the preprocessing is defined and report sensitivity to p.
  2. [Tables 2a and 2b] The table headers mix metric abbreviations and granularity levels (Full, Para., Line, Phrase, Word) in a way that is difficult to parse; please use clearly labeled sub-tables or separate column groups for SCD, SCErr, PPL, and BERT-S.
  3. [Figure 3] The caption says 'Each metric is calculated between two distinct paragraphs' but does not explain what the rows and columns represent or whether diagonal values are within-form comparisons; please label the axes and clarify the construction of the matrices.
  4. [Section 4.3] The phrase 'using both scratch and pre-trained model' should be 'trained both from scratch and from pre-trained weights', and the initialization of newly introduced special-token embeddings in the pre-trained case should be described.
  5. [Section 5.1] The preliminary ChatGPT evaluation reports success rates for producing the correct number of lines, but the interaction between line-count success and syllable-count accuracy is not quantified; please clarify whether the 688 successful samples are filtered before computing SCD/SCErr.

Circularity Check

1 steps flagged · score 2.0 of 10

Evaluation design is mildly self-referential (shared Syllables estimator and BART-summary inputs), but no derivation reduces to its own inputs.

  1. other [Section 4.1 (Dataset), Section 4.2 (Preprocessing), Section 4.4 (Evaluation Metrics)]
    "Syllable counts for each word in the dataset were calculated using the Syllables library [27]. ... For the evaluation set, since there are no available input text-lyrics pairs, we created input-lyrics pairs by summarizing the lyrics with a summarization model [28]."

    The conditioning syllable counts used to train and evaluate the model and the SCD/SCErr metrics used to score it are both computed with the same Syllables estimator. The model is therefore optimized against, and then measured with, the same potentially erroneous definition of syllable count; a systematic error in that estimator would be invisible in the reported accuracy. In addition, the evaluation input text is a BART summary of the target lyric itself, so the text-to-lyrics benchmark measures summary expansion rather than generation from genuinely new input text, making the BERT-S scores self-referential. These are evaluation confounds rather than a derivation that assumes its own conclusion, so they warrant a low score rather than a charge of substantive circularity.

full rationale

This is an empirical system paper rather than a derivation chain, and I found no fitted parameter renamed as a prediction, no equation that assumes its own conclusion, and no load-bearing self-citation: the cited method sources [17,18,19,24,31] are external. The mild circularity is confined to the evaluation design. Section 4.1 states that syllable counts were computed with the Syllables library, and Section 4.4 defines SCD/SCErr on those same counts, so the target conditions and the measured errors share one estimator. Section 4.2 also states that evaluation input text was created by summarizing the very lyric being regenerated, making the text-to-lyrics benchmark a summary-expansion task with self-referential BERT-S scores. These are real confounds, but they do not make the generation results true by construction: the model must still emit tokens that the estimator counts correctly, and a held-out split is used. The large paragraph-level SCErr (68.8%) versus Full SCErr (5.0%) in Table 2a is a reporting and aggregation issue about the strength of the central claim, but it is not itself an instance of circular reasoning. Overall circularity is therefore minor.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new physical or formal entities. Its load-bearing assumptions are all empirical domain assumptions about the reliability of automatic syllable counting, dataset annotations, semantic embeddings, and automatic evaluation metrics. The main free parameters are training or inference configuration choices, none of which are fitted to the headline results.

free parameters (3)
  • Pre-order traversal selection probability p = 0.2 (generation), 0.1 (infilling)
    Hand-chosen probability for randomly selecting subtrees during training plan synthesis (Section 4.2). The value is not fitted to the evaluation metric, but it determines the distribution of training conditions.
  • Toxicity filtering threshold = 0.5
    Lyrics with Detoxify score greater than 0.5 were excluded (Section 4.1). This is a hand-set preprocessing choice that shapes the dataset without affecting the core method.
  • Inference sampling parameters = top-k=20, top-p=0.9, temperature=1.0, repetition penalty=1.2
    Standard sampling settings used at inference (Section 4.3). They affect output diversity and quality but are not fitted to the central claim.
assumptions (5)
  • domain assumption The Syllables library computes correct syllable counts for every word in the training and evaluation sets.
    Syllable counts for all dataset words and all evaluation metrics (SCD, SCErr) rely on this library (Sections 4.1 and 4.4). If it is inaccurate, the quantitative results do not reflect true rhythmic fit.
  • domain assumption Song-form annotations in the Genius lyrics dataset are accurate and consistent.
    The dataset is filtered to lyrics with explicit song-form annotations, and the model conditions on these labels (Section 4.1). Inconsistent annotations would degrade the song-form consistency measurements in Section 5.4.
  • domain assumption SentenceTransformer embeddings of full lyrics adequately represent the semantic content of the input text.
    The semantic embedding is used as a stand-in condition for input text during training and inference (Section 3.1). The validity of the BERT-S evaluation depends on this embedding capturing relevant semantics.
  • domain assumption BART summarization produces input text that is a suitable proxy for real user-provided text in the evaluation set.
    Because no paired text-lyrics data exists, the authors create input text by summarizing the original lyrics (Section 4.2). This assumes summaries are representative of user prompts, which is untested.
  • domain assumption BERT-Score and perplexity are valid proxies for lyric quality and semantic coherence.
    The paper uses BERT-S, PPL, SCD, SCErr, and NLD as its only evaluation metrics (Section 4.4), with no human evaluation. These automatic proxies have known limitations for creative text quality.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Song Form-aware Full-Song Text-to-Lyrics Generation with Multi-Level Granularity Syllable Count Control." pith.science (2026). https://pith.science/paper/DIRLAYTP

@misc{pith2026241113100,
  author       = {Pith},
  title        = {Pith review of: Song Form-aware Full-Song Text-to-Lyrics Generation with Multi-Level Granularity Syllable Count Control},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DIRLAYTP}},
  note         = {Machine review of arXiv:2411.13100}
}
read the original abstract

Lyrics generation presents unique challenges, particularly in achieving precise syllable control while adhering to song form structures such as verses and choruses. Conventional line-by-line approaches often lead to unnatural phrasing, underscoring the need for more granular syllable management. We propose a framework for lyrics generation that enables multi-level syllable control at the word, phrase, line, and paragraph levels, aware of song form. Our approach generates complete lyrics conditioned on input text and song form, ensuring alignment with specified syllable constraints. Generated lyrics samples are available at: https://tinyurl.com/lyrics9999

Figures

Figures reproduced from arXiv: 2411.13100 by the authors.

Figure 1
Figure 1. Multi-level granularity lyrics generation framework. The numbers inside the green boxes indicate the syllable count condition. 2. Related Work Many studies on lyrics generation have attempted to use various features to generate lyrics that match naturally with the melody. Lyrics generation inherits intricate syntactic and semantic chal￾lenges from text generation. Notably, within this domain, there has been a signif… view at source ↗
Figure 2
Figure 2. Training examples of each task. The green tokens represent the tokens directly provided to the model during the inference process, while the red tokens denote the special tokens that the model needs to predict. 3. Methodology 3.1. Multi-Level Granularity Lyrics Generation Our system generates lyrics at various granularities (words, phrases, lines, paragraphs) with precise syllable control. The model is conditioned o… view at source ↗
Figure 3
Figure 3. Evaluation of song form consistency. Each metric is calculated between two distinct paragraphs. trained model. The learning rate was 0.00005 with 500 warm￾up steps, and we used the AdamW optimizer [30]. For all ex￾periments, texts are tokenized using a byte-level Byte Pair En￾coding (BPE) with a vocabulary size of 50,257 and an input size of 1024 consecutive tokens, as in the original GPT-2 model. For inference, we … view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

43 extracted references · 40 canonical work pages

  1. [27]

    Semantics of the unwritten: The effect of end of paragraph and sequence tokens on text generation with GPT2,

    H. Bai, P. Shi, J. Lin, L. Tan, K. Xiong, W. Gao, J. Liu, and M. Li, “Semantics of the unwritten: The effect of end of paragraph and sequence tokens on text generation with GPT2,” in Proc. of ACL- IJCNLP Student Research Workshop 2021, 2021, pp. 148–162

    work page 2021

  2. [1]

    Introduction The field of lyrics information processing [1] presents unique challenges that extend beyond traditional text generation, as lyrics must align with both song form structures (such as verses, choruses, and bridges) and specific syllabic constraints. While natural language generation models have shown promise in gen- erating coherent text, appl...

    work page

  3. [2]

    Song Form-aware Full-Song Text-to-Lyrics Generation with Multi-Level Granularity Syllable Count Control

    Related Work Many studies on lyrics generation have attempted to use various features to generate lyrics that match naturally with the melody. Lyrics generation inherits intricate syntactic and semantic chal- lenges from text generation. Notably, within this domain, there has been a significant increase in the utilization of neural net- works. The procedu...

    work page Pith review arXiv 2025

  4. [3]

    Multi-Level Granularity Lyrics Generation Our system generates lyrics at various granularities (words, phrases, lines, paragraphs) with precise syllable control

    Methodology 3.1. Multi-Level Granularity Lyrics Generation Our system generates lyrics at various granularities (words, phrases, lines, paragraphs) with precise syllable control. The model is conditioned on input text, song structure (e.g., verse, chorus), and syllable constraints. Token Structure. We use a structured token system to align lyrics with son...

    work page

  5. [4]

    Experiments 4.1. Dataset For the training and evaluation of our models, we used the Ge- nius Song Lyrics dataset[25], which comprises approximately 5.1 million multilingual song lyrics, with around 3.3 million lyrics in English. From the dataset, we selectively extracted lyrics featuring explicit song form annotations with each para- graph, aligning with ...

    work page

  6. [5]

    Results 5.1. Preliminary Experiment Large language models (LLMs) such as ChatGPT [34] have demonstrated impressive text generation abilities but often struggle with accurate syllable counting [35]. To evaluate this, we asked an LLM to generate a single paragraph based on in- put text that specified the number of lines and the syllable count per line. A tr...

    work page

  7. [6]

    This method enhances the flexibility and structural accuracy of the generated lyrics

    Conclusion Our study proposes an effective approach to automated lyrics generation and infilling that leverages multi-level granularity for precise syllable control and effective song form manage- ment, all conditioned on an arbitrary input text. This method enhances the flexibility and structural accuracy of the generated lyrics. In future work, we plan ...

    work page

  8. [7]

    RS-2024-00461617, 40%], Information & communica- tions Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) [No

    Acknowledgements This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) [No. RS-2024-00461617, 40%], Information & communica- tions Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) [No. RS-2022-II220320, 2022- 0-00320, 50%], [No.RS-2021-II211343, Artificial I...

    work page 2024

Show all 43 references
  1. [8]

    Lan- guage models are few-shot learners,

    T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhari- wal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al., “Lan- guage models are few-shot learners,”Advances in neural informa- tion processing systems, vol. 33, pp. 1877–1901, 2020

    1901

  2. [9]

    Lyrics information processing: Anal- ysis, generation, and applications,

    K. Watanabe and M. Goto, “Lyrics information processing: Anal- ysis, generation, and applications,” in Proc. of the 1st Workshop on NLP for Music and Audio (NLP4MusA), 2020, pp. 6–12

    2020

  3. [10]

    A melody-conditioned lyrics language model,

    K. Watanabe, Y . Matsubayashi, S. Fukayama, M. Goto, K. Inui, and T. Nakano, “A melody-conditioned lyrics language model,” in Proc. of NAACL-HLT 2018 (Long Papers), 2018, pp. 163–172

    2018

  4. [11]

    A syllable- structured, contextually-based conditionally generation of chinese lyrics,

    X. Lu, J. Wang, B. Zhuang, S. Wang, and J. Xiao, “A syllable- structured, contextually-based conditionally generation of chinese lyrics,” in Proc. of PRICAI 2019. Springer, 2019, pp. 257–265

    2019

  5. [12]

    Songmass: Automatic song writing with pre-training and align- ment constraint,

    Z. Sheng, K. Song, X. Tan, Y . Ren, W. Ye, S. Zhang, and T. Qin, “Songmass: Automatic song writing with pre-training and align- ment constraint,” inProc. of AAAI 2021, vol. 35, no. 15, 2021, pp. 13 798–13 805

    2021

  6. [13]

    Dali: A large dataset of synchronized audio, lyrics and notes, automati- cally created using teacher-student machine learning paradigm,

    G. Meseguer-Brocal, A. Cohen-Hadria, and G. Peeters, “Dali: A large dataset of synchronized audio, lyrics and notes, automati- cally created using teacher-student machine learning paradigm,” arXiv preprint arXiv:1906.10606, 2019

    1906 arXiv

  7. [14]

    Conditional lstm-gan for melody generation from lyrics,

    Y . Yu, A. Srivastava, and S. Canales, “Conditional lstm-gan for melody generation from lyrics,” ACM Transactions on Multi- media Computing, Communications, and Applications (TOMM) , vol. 17, no. 1, pp. 1–20, 2021

    2021

  8. [15]

    Contrastive learning-based audio to lyrics alignment for multiple languages,

    S. Durand, D. Stoller, and S. Ewert, “Contrastive learning-based audio to lyrics alignment for multiple languages,” in Proc. of ICASSP 2023. IEEE, 2023, pp. 1–5

    2023

  9. [16]

    Automatic poetry generation with mutual reinforcement learning,

    X. Yi, M. Sun, R. Li, and W. Li, “Automatic poetry generation with mutual reinforcement learning,” in Proc. of EMNLP 2018 , 2018, pp. 3143–3153

    2018

  10. [17]

    Melody-conditioned lyrics generation with seqgans,

    Y . Chen and A. Lerch, “Melody-conditioned lyrics generation with seqgans,” in 2020 IEEE International Symposium on Mul- timedia (ISM). IEEE, 2020, pp. 189–196

    2020

  11. [18]

    Ai-lyricist: Gener- ating music and vocabulary constrained lyrics,

    X. Ma, Y . Wang, M.-Y . Kan, and W. S. Lee, “Ai-lyricist: Gener- ating music and vocabulary constrained lyrics,” inProceedings of the 29th ACM International Conference on Multimedia, 2021, pp. 1002–1011

    2021

  12. [19]

    Un- supervised melody-to-lyrics generation,

    Y . Tian, A. Narayan-Chen, S. Oraby, A. Cervone, G. Sigurdsson, C. Tao, W. Zhao, Y . Chen, T. Chung, J. Huang, and N. Peng, “Un- supervised melody-to-lyrics generation,” in Proc. of ACL 2023 (Long Papers), 2023, pp. 9235–9254

    2023

  13. [20]

    Seqgan: sequence genera- tive adversarial nets with policy gradient,

    L. Yu, W. Zhang, J. Wang, and Y . Yu, “Seqgan: sequence genera- tive adversarial nets with policy gradient,” inProc. of AAAI 2017, 2017, p. 2852–2858

    2017

  14. [21]

    MASS: Masked se- quence to sequence pre-training for language generation,

    K. Song, X. Tan, T. Qin, J. Lu, and T.-Y . Liu, “MASS: Masked se- quence to sequence pre-training for language generation,” inProc. of ICML 2019, vol. 97. PMLR, 2019, pp. 5926–5936

    2019

  15. [22]

    and pre-trained Vision Transformer [23] for semantic ex- traction in lyrics. We use semantic embeddings of full lyrics as a substitute condition for the input text during training, enabling the model to generate relevant lyrics by analyzing the semantic content of any input te...

  16. [23]

    Chinese poetry generation with recur- rent neural networks,

    X. Zhang and M. Lapata, “Chinese poetry generation with recur- rent neural networks,” in Proc. of EMNLP, 2014, pp. 670–680

    2014

  17. [24]

    Generating chinese classical poems with rnn encoder-decoder,

    X. Yi, R. Li, and M. Sun, “Generating chinese classical poems with rnn encoder-decoder,” inChinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data: 16th China National Conference, CCL 2017, and 5th International Symposium, NLP-NA...

    2017

  18. [25]

    K-pop lyric transla- tion: Dataset, analysis, and neural-modelling,

    H. Kim, J. Jung, D. Jeong, and J. Nam, “K-pop lyric transla- tion: Dataset, analysis, and neural-modelling,” in Proc. of LREC- COLING 2024, 2024, pp. 9974–9987

    2024

  19. [26]

    Automatic song translation for tonal languages,

    F. Guo, C. Zhang, Z. Zhang, Q. He, K. Zhang, J. Xie, and J. Boyd- Graber, “Automatic song translation for tonal languages,” inFind- ings of ACL 2022, 2022, pp. 729–743

    2022

  20. [28]

    Sentence-bert: Sen- tence embeddings using siamese bert-networks,

    N. Reimers and I. Gurevych, “Sentence-bert: Sen- tence embeddings using siamese bert-networks,” in Proc. of EMNLP 2019 , 2019, also available at https: //huggingface.co/sentence-transformers/all-mpnet-base-v2

    2019

  21. [29]

    Text-to-lyrics generation with image- based semantics and reduced risk of plagiarism,

    K. Watanabe and M. Goto, “Text-to-lyrics generation with image- based semantics and reduced risk of plagiarism,” in ISMIR 2023 Hybrid Conference, 2023

    2023

  22. [30]

    High-resolution image synthesis with latent diffusion models,

    R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proc. of CVPR 2022, 2022, pp. 10 684–10 695

    2022

  23. [31]

    An image is worth 16x16 words: Transformers for image recognition at scale,

    A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” in Proc. of ICLR 2021, 2021

    2021

  24. [32]

    Enabling language models to fill in the blanks,

    C. Donahue, M. Lee, and P. Liang, “Enabling language models to fill in the blanks,” in Proc. of ACL 2020, 2020, pp. 2492–2501

    2020

  25. [33]

    Genius song lyrics dataset,

    “Genius song lyrics dataset,” https://www.kaggle.com/datasets/ carlosgdcj/genius-song-lyrics-with-language-information

  26. [34]

    Detoxify,

    L. Hanu and Unitary team, “Detoxify,” Github. https://github.com/unitaryai/detoxify, 2020

    2020

  27. [35]

    Syllables: A fast syllable estimator for python,

    “Syllables: A fast syllable estimator for python,” https://pypi.org/ project/syllables/

  28. [36]

    BART: Denoising sequence-to-sequence pre-training for natural language genera- tion, translation, and comprehension,

    M. Lewis, Y . Liu, N. Goyal, M. Ghazvininejad, A. Mohamed, O. Levy, V . Stoyanov, and L. Zettlemoyer, “BART: Denoising sequence-to-sequence pre-training for natural language genera- tion, translation, and comprehension,” in Proc. of ACL 2020 , 2020, pp. 7871–7880

    2020

  29. [37]

    Language models are unsupervised multitask learners,

    A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog, vol. 1, no. 8, p. 9, 2019, pre-trained checkpoints are available at https://huggingface.co/openai-community/gpt2

    2019

  30. [38]

    Decoupled weight decay regulariza- tion,

    I. Loshchilov and F. Hutter, “Decoupled weight decay regulariza- tion,” in Proc. of ICLR 2019, 2019

    2019

  31. [39]

    A computational evaluation framework for singable lyric translation,

    H. Kim, K. Watanabe, M. Goto, and J. Nam, “A computational evaluation framework for singable lyric translation,” ISMIR 2023 Hybrid Conference, 2023

    2023

  32. [40]

    BERTscore: Evaluating text generation with bert,

    T. Zhang, V . Kishore, F. Wu, K. Q. Weinberger, and Y . Artzi, “BERTscore: Evaluating text generation with bert,”Proc. of ICLR 2020, 2020

    2020

  33. [41]

    Fault-prone java method analysis focusing on pair of lo- cal variables with confusing names,

    K. Tashima, H. Aman, S. Amasaki, T. Yokogawa, and M. Kawa- hara, “Fault-prone java method analysis focusing on pair of lo- cal variables with confusing names,” in 2018 44th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). IEEE, 2018, pp. 154–158

    2018

  34. [42]

    Train- ing language models to follow instructions with human feedback,

    L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Rayet al., “Train- ing language models to follow instructions with human feedback,” Advances in neural information processing systems , vol. 35, pp. 27 730–27 744, 2022

    2022

  35. [43]

    Evaluating large language models on con- trolled generation tasks,

    J. Sun, Y . Tian, W. Zhou, N. Xu, Q. Hu, R. Gupta, J. Wieting, N. Peng, and X. Ma, “Evaluating large language models on con- trolled generation tasks,” in Proc. of EMNLP 2023 , 2023, pp. 3155–3168

    2023

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.