Related papers: Prediction, Selection, and Generation: Exploration of Knowledge-Driven Conversation System

Prediction, Selection, and Generation: Exploration of Knowledge-Driven Conversation System

URL: http://arxiv.org/abs/2104.11454v2
Date: Mon, 26 Apr 2021 02:19:37 GMT
Title: Prediction, Selection, and Generation: Exploration of Knowledge-Driven Conversation System
Authors: Cheng Luo, Dayiheng Liu, Chanjuan Li, Li Lu, Jiancheng Lv
Abstract summary: In open-domain conversational systems, it is important but challenging to leverage background knowledge. We combine the knowledge bases and pre-training model to propose a knowledge-driven conversation system. We study the performance factors that maybe affect the generation of knowledge-driven dialogue.
Score: 24.537862151735006
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: In open-domain conversational systems, it is important but challenging to leverage background knowledge. We can use the incorporation of knowledge to make the generation of dialogue controllable, and can generate more diverse sentences that contain real knowledge. In this paper, we combine the knowledge bases and pre-training model to propose a knowledge-driven conversation system. The system includes modules such as dialogue topic prediction, knowledge matching and dialogue generation. Based on this system, we study the performance factors that maybe affect the generation of knowledge-driven dialogue: topic coarse recall algorithm, number of knowledge choices, generation model choices, etc., and finally made the system reach state-of-the-art. These experimental results will provide some guiding significance for the future research of this task. As far as we know, this is the first work to study and analyze the effects of the related factors.

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