Retrieval-Free Knowledge-Grounded Dialogue Response Generation with Adapters

• URL: http://arxiv.org/abs/2105.06232v1
• Date: Thu, 13 May 2021 12:33:23 GMT
• Title: Retrieval-Free Knowledge-Grounded Dialogue Response Generation with Adapters
• Authors: Yan Xu, Etsuko Ishii, Zihan Liu, Genta Indra Winata, Dan Su, Andrea Madotto, Pascale Fung
• Abstract summary: We propose KnowExpert to bypass the retrieval process by injecting prior knowledge into the pre-trained language models with lightweight adapters. Experimental results show that KnowExpert performs comparably with the retrieval-based baselines.
• Score: 52.725200145600624
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: To diversify and enrich generated dialogue responses, knowledge-grounded dialogue has been investigated in recent years. Despite the success of the existing methods, they mainly follow the paradigm of retrieving the relevant sentences over a large corpus and augment the dialogues with explicit extra information, which is time- and resource-consuming. In this paper, we propose KnowExpert, an end-to-end framework to bypass the retrieval process by injecting prior knowledge into the pre-trained language models with lightweight adapters. To the best of our knowledge, this is the first attempt to tackle this task relying solely on a generation-based approach. Experimental results show that KnowExpert performs comparably with the retrieval-based baselines, demonstrating the potential of our proposed direction.

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