Improving Text Generation with Student-Forcing Optimal Transport
Jianqiao Li, Chunyuan Li, Guoyin Wang, Hao Fu, Yuhchen Lin, Liqun Chen, Yizhe Zhang, Chenyang Tao, Ruiyi Zhang, Wenlin Wang, Dinghan Shen, Qian Yang, Lawrence Carin
Abstract
Neural language models are often trained with maximum likelihood estimation (MLE), where the next word is generated conditioned on the ground-truth word tokens. During testing, however, the model is instead conditioned on previously generated tokens, resulting in what is termed exposure bias. To reduce this gap between training and testing, we propose using optimal transport (OT) to match the sequences generated in these two modes. We examine the necessity of adding Student-Forcing scheme during training with an imitation learning interpretation. An extension is further proposed to improve the OT learning for long sequences, based on the structural and contextual information of the text sequences. The effectiveness of the proposed method is validated on machine translation, text summarization, and text generation tasks.- Anthology ID:
- 2020.emnlp-main.735
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 9144–9156
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.735
- DOI:
- 10.18653/v1/2020.emnlp-main.735
- Cite (ACL):
- Jianqiao Li, Chunyuan Li, Guoyin Wang, Hao Fu, Yuhchen Lin, Liqun Chen, Yizhe Zhang, Chenyang Tao, Ruiyi Zhang, Wenlin Wang, Dinghan Shen, Qian Yang, and Lawrence Carin. 2020. Improving Text Generation with Student-Forcing Optimal Transport. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 9144–9156, Online. Association for Computational Linguistics.
- Cite (Informal):
- Improving Text Generation with Student-Forcing Optimal Transport (Li et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2020.emnlp-main.735.pdf