@inproceedings{li-etal-2022-back,
title = "Back to the Future: Bidirectional Information Decoupling Network for Multi-turn Dialogue Modeling",
author = "Li, Yiyang and
Zhao, Hai and
Zhang, Zhuosheng",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.177",
doi = "10.18653/v1/2022.emnlp-main.177",
pages = "2761--2774",
abstract = "Multi-turn dialogue modeling as a challenging branch of natural language understanding (NLU), aims to build representations for machines to understand human dialogues, which provides a solid foundation for multiple downstream tasks. Recent studies of dialogue modeling commonly employ pre-trained language models (PrLMs) to encode the dialogue history as successive tokens, which is insufficient in capturing the temporal characteristics of dialogues. Therefore, we propose Bidirectional Information Decoupling Network (BiDeN) as a universal dialogue encoder, which explicitly incorporates both the past and future contexts and can be generalized to a wide range of dialogue-related tasks. Experimental results on datasets of different downstream tasks demonstrate the universality and effectiveness of our BiDeN.",
}
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%0 Conference Proceedings
%T Back to the Future: Bidirectional Information Decoupling Network for Multi-turn Dialogue Modeling
%A Li, Yiyang
%A Zhao, Hai
%A Zhang, Zhuosheng
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F li-etal-2022-back
%X Multi-turn dialogue modeling as a challenging branch of natural language understanding (NLU), aims to build representations for machines to understand human dialogues, which provides a solid foundation for multiple downstream tasks. Recent studies of dialogue modeling commonly employ pre-trained language models (PrLMs) to encode the dialogue history as successive tokens, which is insufficient in capturing the temporal characteristics of dialogues. Therefore, we propose Bidirectional Information Decoupling Network (BiDeN) as a universal dialogue encoder, which explicitly incorporates both the past and future contexts and can be generalized to a wide range of dialogue-related tasks. Experimental results on datasets of different downstream tasks demonstrate the universality and effectiveness of our BiDeN.
%R 10.18653/v1/2022.emnlp-main.177
%U https://aclanthology.org/2022.emnlp-main.177
%U https://doi.org/10.18653/v1/2022.emnlp-main.177
%P 2761-2774
Markdown (Informal)
[Back to the Future: Bidirectional Information Decoupling Network for Multi-turn Dialogue Modeling](https://aclanthology.org/2022.emnlp-main.177) (Li et al., EMNLP 2022)
ACL