DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation

Wei Chen, Yeyun Gong, Song Wang, Bolun Yao, Weizhen Qi, Zhongyu Wei, Xiaowu Hu, Bartuer Zhou, Yi Mao, Weizhu Chen, Biao Cheng, Nan Duan


Abstract
Dialog response generation in open domain is an important research topic where the main challenge is to generate relevant and diverse responses. In this paper, we propose a new dialog pre-training framework called DialogVED, which introduces continuous latent variables into the enhanced encoder-decoder pre-training framework to increase the relevance and diversity of responses. With the help of a large dialog corpus (Reddit), we pre-train the model using the following 4 tasks, used in training language models (LMs) and Variational Autoencoders (VAEs) literature: 1) masked language model; 2) response generation; 3) bag-of-words prediction; and 4) KL divergence reduction. We also add additional parameters to model the turn structure in dialogs to improve the performance of the pre-trained model. We conduct experiments on PersonaChat, DailyDialog, and DSTC7-AVSD benchmarks for response generation. Experimental results show that our model achieves the new state-of-the-art results on all these datasets.
Anthology ID:
2022.acl-long.333
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4852–4864
Language:
URL:
https://aclanthology.org/2022.acl-long.333
DOI:
10.18653/v1/2022.acl-long.333
Bibkey:
Cite (ACL):
Wei Chen, Yeyun Gong, Song Wang, Bolun Yao, Weizhen Qi, Zhongyu Wei, Xiaowu Hu, Bartuer Zhou, Yi Mao, Weizhu Chen, Biao Cheng, and Nan Duan. 2022. DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4852–4864, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation (Chen et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.333.pdf
Code
 lemuria-wchen/DialogVED
Data
DSTC7 Task 2DailyDialog