Coarse-to-Fine Knowledge Selection for Document Grounded Dialogs

• URL: http://arxiv.org/abs/2302.11849v1
• Date: Thu, 23 Feb 2023 08:28:29 GMT
• Title: Coarse-to-Fine Knowledge Selection for Document Grounded Dialogs
• Authors: Yeqin Zhang, Haomin Fu, Cheng Fu, Haiyang Yu, Yongbin Li, Cam-Tu Nguyen
• Abstract summary: Multi-document grounded dialogue systems (DGDS) answer users' requests by finding supporting knowledge from a collection of documents. This paper proposes Re3G, which aims to optimize both coarse-grained knowledge retrieval and fine-grained knowledge extraction in a unified framework.
• Score: 11.63334863772068
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-document grounded dialogue systems (DGDS) belong to a class of conversational agents that answer users' requests by finding supporting knowledge from a collection of documents. Most previous studies aim to improve the knowledge retrieval model or propose more effective ways to incorporate external knowledge into a parametric generation model. These methods, however, focus on retrieving knowledge from mono-granularity language units (e.g. passages, sentences, or spans in documents), which is not enough to effectively and efficiently capture precise knowledge in long documents. This paper proposes Re3G, which aims to optimize both coarse-grained knowledge retrieval and fine-grained knowledge extraction in a unified framework. Specifically, the former efficiently finds relevant passages in a retrieval-and-reranking process, whereas the latter effectively extracts finer-grain spans within those passages to incorporate into a parametric answer generation model (BART, T5). Experiments on DialDoc Shared Task demonstrate the effectiveness of our method.

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