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Conference Paper/Proceeding/Abstract 318 views

Rare-Class Dialogue Act Tagging for Alzheimer’s Disease Diagnosis

Shamila Nasreen, Julian Hough Orcid Logo, Matthew Purver

Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue, Pages: 290 - 300

Swansea University Author: Julian Hough Orcid Logo

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DOI (Published version): 10.18653/v1/2021.sigdial-1.32

Abstract

Alzheimer’s Disease (AD) is associated with many characteristic changes, not only in an individual’s language but also in the interactive patterns observed in dialogue. The most indicative changes of this latter kind tend to be associated with relatively rare dialogue acts (DAs), such as those invol...

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Published in: Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue
ISBN: 978-1-954085-81-7
Published: Stroudsburg, PA, USA Association for Computational Linguistics 2021
URI: https://cronfa.swan.ac.uk/Record/cronfa64935
first_indexed 2023-11-07T22:24:53Z
last_indexed 2024-11-25T14:15:02Z
id cronfa64935
recordtype SURis
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spelling 2024-03-12T14:12:47.1416356 v2 64935 2023-11-07 Rare-Class Dialogue Act Tagging for Alzheimer’s Disease Diagnosis 082d773ae261d2bbf49434dd2608ab40 0000-0002-4345-6759 Julian Hough Julian Hough true false 2023-11-07 MACS Alzheimer’s Disease (AD) is associated with many characteristic changes, not only in an individual’s language but also in the interactive patterns observed in dialogue. The most indicative changes of this latter kind tend to be associated with relatively rare dialogue acts (DAs), such as those involved in clarification exchanges and responses to particular kinds of questions. However, most existing work in DA tagging focuses on improving average performance, effectively prioritizing more frequent classes; it thus gives a poor performance on these rarer classes and is not suited for application to AD analysis. In this paper, we investigate tagging specifically for rare class DAs, using a hierarchical BiLSTM model with various ways of incorporating information from previous utterances and DA tags in context. We show that this can give good performance for rare DA classes on both the general Switchboard corpus (SwDA) and an AD-specific conversational dataset, the Carolinas Conversation Collection (CCC); and that the tagger outputs then contribute useful information for distinguishing patients with and without AD. Conference Paper/Proceeding/Abstract Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue 290 300 Association for Computational Linguistics Stroudsburg, PA, USA 978-1-954085-81-7 29 7 2021 2021-07-29 10.18653/v1/2021.sigdial-1.32 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Not Required 2024-03-12T14:12:47.1416356 2023-11-07T22:19:21.4983077 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Shamila Nasreen 1 Julian Hough 0000-0002-4345-6759 2 Matthew Purver 3
title Rare-Class Dialogue Act Tagging for Alzheimer’s Disease Diagnosis
spellingShingle Rare-Class Dialogue Act Tagging for Alzheimer’s Disease Diagnosis
Julian Hough
title_short Rare-Class Dialogue Act Tagging for Alzheimer’s Disease Diagnosis
title_full Rare-Class Dialogue Act Tagging for Alzheimer’s Disease Diagnosis
title_fullStr Rare-Class Dialogue Act Tagging for Alzheimer’s Disease Diagnosis
title_full_unstemmed Rare-Class Dialogue Act Tagging for Alzheimer’s Disease Diagnosis
title_sort Rare-Class Dialogue Act Tagging for Alzheimer’s Disease Diagnosis
author_id_str_mv 082d773ae261d2bbf49434dd2608ab40
author_id_fullname_str_mv 082d773ae261d2bbf49434dd2608ab40_***_Julian Hough
author Julian Hough
author2 Shamila Nasreen
Julian Hough
Matthew Purver
format Conference Paper/Proceeding/Abstract
container_title Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue
container_start_page 290
publishDate 2021
institution Swansea University
isbn 978-1-954085-81-7
doi_str_mv 10.18653/v1/2021.sigdial-1.32
publisher Association for Computational Linguistics
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
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description Alzheimer’s Disease (AD) is associated with many characteristic changes, not only in an individual’s language but also in the interactive patterns observed in dialogue. The most indicative changes of this latter kind tend to be associated with relatively rare dialogue acts (DAs), such as those involved in clarification exchanges and responses to particular kinds of questions. However, most existing work in DA tagging focuses on improving average performance, effectively prioritizing more frequent classes; it thus gives a poor performance on these rarer classes and is not suited for application to AD analysis. In this paper, we investigate tagging specifically for rare class DAs, using a hierarchical BiLSTM model with various ways of incorporating information from previous utterances and DA tags in context. We show that this can give good performance for rare DA classes on both the general Switchboard corpus (SwDA) and an AD-specific conversational dataset, the Carolinas Conversation Collection (CCC); and that the tagger outputs then contribute useful information for distinguishing patients with and without AD.
published_date 2021-07-29T02:43:54Z
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score 11.04748

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