Open AccessMulti-Turn Response Selection in Retrieval Based Chatbots with Hierarchical Residual Matching Network
Author(s) -
Zhuo Zhang,
Danyang Zheng,
Ping Gong
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1757/1/012023
Subject(s) - computer science , selection (genetic algorithm) , conversation , matching (statistics) , residual , context (archaeology) , benchmark (surveying) , task (project management) , question answering , semantic similarity , artificial intelligence , information retrieval , chatbot , machine learning , algorithm , statistics , mathematics , paleontology , philosophy , linguistics , management , geodesy , economics , biology , geography
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.