Memory-Based Matching Models for Multi-turn Response Selection in Retrieval-Based Chatbots

  • Xingwu Lu
  • , Man Lan*
  • , Yuanbin Wu
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

This paper describes the system we submitted to Task 5 in NLPCC 2018, i.e., Multi-Turn Dialogue System in Open-Domain. This work focuses on the second subtask: Retrieval Dialogue System. Given conversation sessions and 10 candidates for each dialogue session, this task is to select the most appropriate response from candidates. We design a memory-based matching network integrating sequential matching network and several NLP features together to address this task. Our system finally achieves the precision of 62.61% on test set of NLPCC 2018 subtask 2 and officially released results show that our system ranks 1st among all the participants.

Original languageEnglish
Title of host publicationNatural Language Processing and Chinese Computing - 7th CCF International Conference, NLPCC 2018, Proceedings
EditorsDongyan Zhao, Sujian Li, Min Zhang, Vincent Ng, Hongying Zan
PublisherSpringer Verlag
Pages269-278
Number of pages10
ISBN (Print)9783319994949
DOIs
StatePublished - 2018
Event7th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2018 - Hohhot, China
Duration: 26 Aug 201830 Aug 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11108 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2018
Country/TerritoryChina
CityHohhot
Period26/08/1830/08/18

Keywords

  • Multi-turn conversation
  • Neural networks
  • Response selection

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