Denoising based Sequence-to-Sequence Pre-training for Text Generation

Liang Wang, Wei Zhao, Ruoyu Jia, Sujian Li, Jingming Liu

Abstract
This paper presents a new sequence-to-sequence (seq2seq) pre-training method PoDA (Pre-training of Denoising Autoencoders), which learns representations suitable for text generation tasks. Unlike encoder-only (e.g., BERT) or decoder-only (e.g., OpenAI GPT) pre-training approaches, PoDA jointly pre-trains both the encoder and decoder by denoising the noise-corrupted text, and it also has the advantage of keeping the network architecture unchanged in the subsequent fine-tuning stage. Meanwhile, we design a hybrid model of Transformer and pointer-generator networks as the backbone architecture for PoDA. We conduct experiments on two text generation tasks: abstractive summarization, and grammatical error correction. Results on four datasets show that PoDA can improve model performance over strong baselines without using any task-specific techniques and significantly speed up convergence.
Anthology ID:
D19-1412
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
4003–4015
Language:
URL:
https://preview.aclanthology.org/add_missing_videos/D19-1412/
DOI:
10.18653/v1/D19-1412
Bibkey:
Cite (ACL):
Liang Wang, Wei Zhao, Ruoyu Jia, Sujian Li, and Jingming Liu. 2019. Denoising based Sequence-to-Sequence Pre-training for Text Generation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 4003–4015, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Denoising based Sequence-to-Sequence Pre-training for Text Generation (Wang et al., EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/add_missing_videos/D19-1412.pdf
Data
Billion Word Benchmark, JFLEG
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