Composing Ci with Reinforced Non-autoregressive Text Generation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to compose Ci are limited in handling the constraints of tune patterns . authors propose a non-autoregressive approach to generate Ci using a synchronous process . |
| Approach: | They propose to compose Ci using a non-autoregressive approach that takes into account rigid formats . they propose to apply reinforcement learning to the generation process with rigid constraints . |
| Outcome: | The proposed method outperforms baselines and previous studies on a Ci dataset . it allows the model to perform synchronous generation while maintaining the format and content requirement. |
Similar Papers
Rigid Formats Controlled Text Generation (2020.acl-main)
Copied to clipboard
| Challenge: | Neural text generation is a challenging task that requires rigid formats to be controlled . a framework called SongNet is designed to tackle this problem . |
| Approach: | They propose a framework to tackle a task called rigid formats controlled text generation . they propose rhyming schemes and a transformer-based auto-regressive language model to improve the modeling performance . |
| Outcome: | The proposed framework improves the performance on format, rhyme, and sentence integrity. |
PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods for text generation still suffer from incoherence problems . Neural sequence-to-sequence (seq2sequ) models generate fluent results . |
| Approach: | They propose a novel generation framework that leverages autoregressive self-attention mechanism to conduct content planning and surface realization dynamically. |
| Outcome: | The proposed framework outperforms baseline models and generates more coherent texts with richer contents. |
SongComposer: A Large Language Model for Lyric and Melody Generation in Song Composition (2025.acl-long)
Copied to clipboard
Shuangrui Ding, Zihan Liu, Xiaoyi Dong, Pan Zhang, Rui Qian, Junhao Huang, Conghui He, Dahua Lin, Jiaqi Wang
| Challenge: | Creating lyrics and melodies in symbolic format requires expert knowledge of melody and an advanced understanding of lyrics. |
| Approach: | They introduce SongComposer, a music-specialized large language model that can create symbolic lyrics and melodies following instructions. |
| Outcome: | The proposed model outperforms existing models in symbolic song composition tasks. |
From Scaffolding to Assimilation: Progressive Structural Internalization for Format-Constrained Creative Text Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing paradigms rely on unreliable prompting or rigid constrained decoding strategies to achieve aesthetic unity. |
| Approach: | They propose a framework to embed external constraints into the model’s intrinsic intuition and use it to generate open-ended creative texts. |
| Outcome: | The proposed framework surpasses baselines in both strict constraint adherence and literary aesthetics. |
Directed Acyclic Transformer Pre-training for High-quality Non-autoregressive Text Generation (2023.tacl-1)
Copied to clipboard
| Challenge: | Existing non-AutoRegressive (NAR) text generation models lack proper pre-training, making them far behind pre-trained autoregressive models. |
| Approach: | They propose a novel pre-training task to promote prediction consistency in non-autoregressive (NAR) generation. |
| Outcome: | The proposed model outperforms existing pre-trained models and achieves 17 times speedup in throughput. |
Non-Autoregressive Text Generation with Pre-trained Language Models (2021.eacl-main)
Copied to clipboard
| Challenge: | Autoregressive generation models generate tokens in a left-to-right, token-by-token fashion, resulting in lag in inference. |
| Approach: | They propose to use BERT as the backbone of a non-autoregressive generation model for greatly improved performance. |
| Outcome: | The proposed model outperforms existing non-autoregressive models and achieves competitive performance with many strong autoregressive model. |
Anchored Cyclic Generation: A Novel Paradigm for Long-Sequence Symbolic Music Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | Autoregressive sequence modeling has been successful in many domains, but maintaining long-term coherence and structural integrity remains a challenge. |
| Approach: | They propose an ACG paradigm that relies on anchor features from previously generated musical content to guide subsequent generation during the autoregressive process. |
| Outcome: | The proposed framework outperforms existing methods in symbolic music generation tasks. |
CIE: Controlling Language Model Text Generations Using Continuous Signals (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to control language models with intent are brittle and hard to scale. |
| Approach: | They propose to use a set of LMs to fine-tune to expect a control vector that is interpolated between a "low" and a 'high' token embedding. |
| Outcome: | The proposed method can be finetuned to expect a control vector that is interpolated between a “low” and a ‘high” token embedding. |
Unsupervised Melody-to-Lyrics Generation (2023.acl-long)
Copied to clipboard
Yufei Tian, Anjali Narayan-Chen, Shereen Oraby, Alessandra Cervone, Gunnar Sigurdsson, Chenyang Tao, Wenbo Zhao, Tagyoung Chung, Jing Huang, Nanyun Peng
| Challenge: | Existing methods for automatic melody-to-lyric generation are limited due to the limited amount of melody-lyrical aligned data. |
| Approach: | They propose a method for automatic melody-to-lyric generation without training on any aligned melody-lyr data. |
| Outcome: | The proposed model generates high-quality lyrics that are singable, intelligible, and coherent than baseline models. |
Non-Autoregressive Sequence Generation (2022.acl-tutorials)
Copied to clipboard
| Challenge: | Non-autoregressive sequence generation (NAR) models generate output sequences in parallel to speed up generation process. |
| Approach: | This tutorial provides a thorough introduction and review of non-autoregressive sequence generation . it aims to generate the entire or partial output sequences in parallel to speed up the generation process . |
| Outcome: | This tutorial provides a thorough introduction and review of non-autoregressive sequence generation . it aims to reduce the performance gap between state-of-the-art models due to lack of modeling power . |