| Challenge: | Rap is a vocal style rooted in Hip-Hop culture, characterized by producing rhymes in synchrony with a rhythmic beat. |
| Approach: | They propose a method for generating Japanese rap lyrics with a large language model . the model's rhyming behavior is improved by using existing Japanese rhapsodysts as training data. |
| Outcome: | The proposed method improves outputs that receive moderate or high human ratings on rhyme-related criteria. |
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Syllable-level lyrics generation from melody exploiting character-level language model (2024.findings-eacl)
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| Challenge: | Pre-trained language models specifically designed at the syllable level are not available. |
| Approach: | They propose to exploit character-level language models for syllable-level lyrics generation from symbolic melody. |
| Outcome: | The proposed system improves coherence and correctness of generated lyrics without training expensive language models. |
DeepRapper: Neural Rap Generation with Rhyme and Rhythm Modeling (2021.acl-long)
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| Challenge: | Existing systems for rap generation focus on rhyming lyrics but ignore rhythmic beats . rap lyrics need to be semantically meaningful and fashionable to convey interesting stories . |
| Approach: | They develop a Transformer-based rap generation system that can model both rhymes and rhythms. |
| Outcome: | The proposed system generates high-quality raps with rhymes and rhythms . it is based on a Transformer-based language model . |
A Melody-Conditioned Lyrics Language Model (N18-1)
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Kento Watanabe, Yuichiroh Matsubayashi, Satoru Fukayama, Masataka Goto, Kentaro Inui, Tomoyasu Nakano
| Challenge: | Existing models for lyrics generation are insufficient to capture relationship between lyrics and melody. |
| Approach: | They propose a data-driven language model that generates entire lyrics for a given melody. |
| Outcome: | The proposed model generates fluent lyrics while maintaining compatibility between lyrics and melodies. |
Sudowoodo: A Chinese Lyric Imitation System with Source Lyrics (2023.emnlp-demo)
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| Challenge: | Existing studies on lyrics generation focus on generating accurate lyrics using keywords, rhymes, etc. However, there is no parallel corpus for lyrics imitation. |
| Approach: | They propose a Chinese lyrics imitation system that can generate new lyrics based on source lyrics. |
| Outcome: | The proposed system can generate new lyrics based on the source lyrics . human evaluation shows it can perform better lyric imitation. |
Automatic Poetry Generation with Mutual Reinforcement Learning (D18-1)
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| Challenge: | Existing models for automatic poetry generation are based on maximum likelihood estimation (MLE) MLE-based models tend to remember common patterns of the poetry corpus, which results in loss-evaluation mismatch. |
| Approach: | They propose to model the criteria and use them as explicit rewards to guide gradient update by reinforcement learning to motivate the model to pursue higher scores. |
| Outcome: | The proposed model outperforms the current state-of-the-art model and improves on Chinese poetry. |
Reinforcement Learning with Token-level Feedback for Controllable Text Generation (2024.findings-naacl)
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| Challenge: | Existing methods for controllable text generation are guided by coarse-grained feedback, which may lead to suboptimal performance owing to semantic twists or progressions within sentences. |
| Approach: | They propose a reinforcement learning algorithm which formulates TOken-LEvel rewards for controllable text generation and employs a "first-quantize-then-noise" paradigm to enhance the robustness of the RL algorithm. |
| Outcome: | The proposed algorithm can achieve superior performance on single-attribute and multi-attract control tasks. |
SongRewriter: A Chinese Song Rewriting System with Controllable Content and Rhyme Scheme (2023.findings-acl)
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| Challenge: | Existing methods of generating singable lyrics are based on a given melody, but there are two main challenges: generating the lyrics without knowing the melody and composing compatible melodies. |
| Approach: | They propose a Chinese lyric generation and editing system which rewrites lyrics of an existing song such that they are compatible with the rhythm of the existing melody. |
| Outcome: | The proposed system is based on a randomized multi-level masking strategy and can generate new lyrics or edit fragments without prior knowledge of melody composition. |
UniLG: A Unified Structure-aware Framework for Lyrics Generation (2023.acl-long)
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| Challenge: | Existing works ignore musical attributes hidden behind lyrics and structure of lyrics . existing works ignore structure of generated lyrics and do not consider structure of songs . |
| Approach: | They propose a framework for conditional lyrics generation that considers structure and relationship between lyrics and music. |
| Outcome: | The proposed framework improves the structure modeling and unifies different conditions for different types of lyrics generation. |
RS-DPO: A Hybrid Rejection Sampling and Direct Preference Optimization Method for Alignment of Large Language Models (2024.findings-naacl)
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| Challenge: | Reinforcement learning with human feedback (RLHF) is widely employed to align large language models with user intent. |
| Approach: | They propose to combine rejection sampling and direct preference optimization to improve alignment with user intent by identifying pairs of contrastive samples from human annotator and alternative LLMs. |
| Outcome: | The proposed method outperforms existing methods including RS, PPO, and DPO in a limited resource environment. |
Scansion-based Lyrics Generation (2024.lrec-main)
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| Challenge: | a new method for generating lyrics for Mandarin songs is based on scansion . the number of syllables required is variable due to the number and number of notes . |
| Approach: | They propose a method to generate Mandarin lyrics with a good match between melody and tonal contour. |
| Outcome: | The proposed system outperforms all other systems in lyric-melody fit and uses proxies for quantifying creativity. |