Abstract
Teacher-forcing is widely used in training sequence generation models to improve sampling efficiency and to stabilize training. However, teacher-forcing is vulnerable to the exposure bias problem. Previous works have attempted to address exposure bias by modifying the training data to simulate model-generated results. Nevertheless, they do not consider the pairwise relationship between the original training data and the modified ones, which provides more information during training. Hence, we propose Regularized Teacher-Forcing (R-TeaFor) to utilize this relationship for better regularization. Empirically, our experiments show that R-TeaFor outperforms previous summarization state-of-the-art models, and the results can be generalized to different pre-trained models.- Anthology ID:
- 2022.emnlp-main.423
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6303–6311
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.423
- DOI:
- 10.18653/v1/2022.emnlp-main.423
- Cite (ACL):
- Guan-Yu Lin and Pu-Jen Cheng. 2022. R-TeaFor: Regularized Teacher-Forcing for Abstractive Summarization. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 6303–6311, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- R-TeaFor: Regularized Teacher-Forcing for Abstractive Summarization (Lin & Cheng, EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/landing_page/2022.emnlp-main.423.pdf