Getting Your Conversation on Track: Estimation of Residual Life for Conversations

Zexin Lu, Jing Li, Yingyi Zhang, Haisong Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


This paper presents a predictive study on the progress of conversations. Specifically, we estimate the residual life for conversations, which is defined as the count of new turns to occur in a conversation thread. While most previous work focus on coarse-grained estimation that classifies the number of coming turns into two categories, we study fine-grained categorization for varying lengths of residual life. To this end, we propose a hierarchical neural model that jointly explores indicative representations from the content in turns and the structure of conversations in an end-to-end manner. Extensive experiments on both human-human and human-machine conversations demonstrate the superiority of our proposed model and its potential helpfulness in chatbot response selection.

Original languageEnglish
Title of host publication2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781728170664
Publication statusPublished - 19 Jan 2021
Event2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Virtual, Shenzhen, China
Duration: 19 Jan 202122 Jan 2021

Publication series

Name2021 IEEE Spoken Language Technology Workshop, SLT 2021 - Proceedings


Conference2021 IEEE Spoken Language Technology Workshop, SLT 2021
CityVirtual, Shenzhen


  • Conversation Understanding
  • Dialogue System
  • Natural Language Processing
  • Social Computing
  • User Behavior Analysis

ASJC Scopus subject areas

  • Linguistics and Language
  • Language and Linguistics
  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture

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