Multi-Turn Response Selection in Retrieval Based Chatbots with Hierarchical Residual Matching Network. Issue 1 (January 2021)

• Record Type: Journal Article
• Authors: Zhang, Zhuo; Zheng, Danyang; Gong, Ping
• Is Part Of: Journal of physics. Volume 1757:Issue 1(2021)
• Publication Date: 2021-01
• Subjects: Multi-turn response selection -- hierarchical semantic information -- residual matching; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1757/1/012023
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Response selection in retrieval-based chatbot aims to find the most relevant response in a candidate repository given the conversation context. A key technique to this task lies in how to measure the matching degree between conversation context and response at rich semantic information. In this paper, we propose a hierarchical residual matching network (HRMN) to fully extract and make use of the rich semantic information in the conversation history and response for themulti-turn response selection task. We empirically verify HRMN on two benchmark data sets and compare against advanced approaches. Evaluation results demonstrate that HRMN outperforms strong baselines and has a distinct improvement in response selection.