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Find Supports for the Post about Mental Issues: More Than Semantic Matching

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Published:12 November 2022Publication History
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Abstract

Mental-health-oriented question-answering (MH-QA) aims at retrieving an appropriate response for a question post involving mental health issues, which will be used to assist counsellors to reply to the support seeker. This task is different from the general QA task because many additional criteria such as emotions are involved. In this paper, we propose the Multi-Feature Graph Convolutional Network model (MF-GCN) to integrate not only semantic features, but also mental health related features and context features, to match question posts and responses. Different types of feature are exploited through multiple graph convolutional networks. A new dataset is constructed for MH-QA to evaluate our model. Experimental results show that our model with mental health features significantly outperforms a wide range of state-of-the-art models without them.

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    • Published in

      cover image ACM Transactions on Asian and Low-Resource Language Information Processing
      ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 21, Issue 6
      November 2022
      372 pages
      ISSN:2375-4699
      EISSN:2375-4702
      DOI:10.1145/3568970
      Issue’s Table of Contents

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Publication History

      • Published: 12 November 2022
      • Online AM: 3 February 2022
      • Accepted: 25 December 2021
      • Received: 15 September 2021
      Published in tallip Volume 21, Issue 6

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