Response Selection for Multi-Party Conversations with Dynamic Topic Tracking

Weishi Wang, Steven C.H. Hoi, Shafiq Joty


Abstract
While participants in a multi-party multi-turn conversation simultaneously engage in multiple conversation topics, existing response selection methods are developed mainly focusing on a two-party single-conversation scenario. Hence, the prolongation and transition of conversation topics are ignored by current methods. In this work, we frame response selection as a dynamic topic tracking task to match the topic between the response and relevant conversation context. With this new formulation, we propose a novel multi-task learning framework that supports efficient encoding through large pretrained models with only two utterances at once to perform dynamic topic disentanglement and response selection. We also propose Topic-BERT an essential pretraining step to embed topic information into BERT with self-supervised learning. Experimental results on the DSTC-8 Ubuntu IRC dataset show state-of-the-art results in response selection and topic disentanglement tasks outperforming existing methods by a good margin.
Anthology ID:
2020.emnlp-main.533
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6581–6591
Language:
URL:
https://aclanthology.org/2020.emnlp-main.533
DOI:
10.18653/v1/2020.emnlp-main.533
Bibkey:
Cite (ACL):
Weishi Wang, Steven C.H. Hoi, and Shafiq Joty. 2020. Response Selection for Multi-Party Conversations with Dynamic Topic Tracking. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6581–6591, Online. Association for Computational Linguistics.
Cite (Informal):
Response Selection for Multi-Party Conversations with Dynamic Topic Tracking (Wang et al., EMNLP 2020)
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PDF:
https://aclanthology.org/2020.emnlp-main.533.pdf
Video:
 https://slideslive.com/38939032