Response Selection for Multi-Party Conversations with Dynamic Topic Tracking

• URL: http://arxiv.org/abs/2010.07785v1
• Date: Thu, 15 Oct 2020 14:21:38 GMT
• Title: Response Selection for Multi-Party Conversations with Dynamic Topic Tracking
• Authors: Weishi Wang, Shafiq Joty, Steven C.H. Hoi
• Abstract summary: We frame response selection as a dynamic topic tracking task to match the topic between the response and relevant conversation context. We propose a novel multi-task learning framework that supports efficient encoding through large pretrained models. Experimental results on the DSTC-8 Ubuntu IRC dataset show state-of-the-art results in response selection and topic disentanglement tasks.
• Score: 63.15158355071206
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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