Distilling Knowledge Learned in BERT for Text Generation

Yen-Chun Chen, Zhe Gan, Yu Cheng, Jingzhou Liu, Jingjing Liu


Abstract
Large-scale pre-trained language model such as BERT has achieved great success in language understanding tasks. However, it remains an open question how to utilize BERT for language generation. In this paper, we present a novel approach, Conditional Masked Language Modeling (C-MLM), to enable the finetuning of BERT on target generation tasks. The finetuned BERT (teacher) is exploited as extra supervision to improve conventional Seq2Seq models (student) for better text generation performance. By leveraging BERT’s idiosyncratic bidirectional nature, distilling knowledge learned in BERT can encourage auto-regressive Seq2Seq models to plan ahead, imposing global sequence-level supervision for coherent text generation. Experiments show that the proposed approach significantly outperforms strong Transformer baselines on multiple language generation tasks such as machine translation and text summarization. Our proposed model also achieves new state of the art on IWSLT German-English and English-Vietnamese MT datasets.
Anthology ID:
2020.acl-main.705
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7893–7905
Language:
URL:
https://aclanthology.org/2020.acl-main.705
DOI:
10.18653/v1/2020.acl-main.705
Bibkey:
Cite (ACL):
Yen-Chun Chen, Zhe Gan, Yu Cheng, Jingzhou Liu, and Jingjing Liu. 2020. Distilling Knowledge Learned in BERT for Text Generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7893–7905, Online. Association for Computational Linguistics.
Cite (Informal):
Distilling Knowledge Learned in BERT for Text Generation (Chen et al., ACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.705.pdf
Video:
 http://slideslive.com/38929146
Code
 ChenRocks/Distill-BERT-Textgen