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

Shamila Nasreen, Julian Hough, 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

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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
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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 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.
College: Faculty of Science and Engineering
Start Page: 290
End Page: 300