@inproceedings{agarwal-etal-2024-analysing,
title = "Analysing relevance of Discourse Structure for Improved Mental Health Estimation",
author = {Agarwal, Navneet and
Dias, Ga{\"e}l and
Dollfus, Sonia},
editor = "Yates, Andrew and
Desmet, Bart and
Prud{'}hommeaux, Emily and
Zirikly, Ayah and
Bedrick, Steven and
MacAvaney, Sean and
Bar, Kfir and
Ireland, Molly and
Ophir, Yaakov",
booktitle = "Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)",
month = mar,
year = "2024",
address = "St. Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.clpsych-1.9",
pages = "127--132",
abstract = "Automated depression estimation has received significant research attention in recent years as a result of its growing impact on the global community. Within the context of studies based on patient-therapist interview transcripts, most researchers treat the dyadic discourse as a sequence of unstructured sentences, thus ignoring the discourse structure within the learning process. In this paper we propose Multi-view architectures that divide the input transcript into patient and therapist views based on sentence type in an attempt to utilize symmetric discourse structure for improved model performance. Experiments on DAIC-WOZ dataset for binary classification task within depression estimation show advantages of Multi-view architecture over sequential input representations. Our model also outperforms the current state-of-the-art results and provide new SOTA performance on test set of DAIC-WOZ dataset.",
}
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<abstract>Automated depression estimation has received significant research attention in recent years as a result of its growing impact on the global community. Within the context of studies based on patient-therapist interview transcripts, most researchers treat the dyadic discourse as a sequence of unstructured sentences, thus ignoring the discourse structure within the learning process. In this paper we propose Multi-view architectures that divide the input transcript into patient and therapist views based on sentence type in an attempt to utilize symmetric discourse structure for improved model performance. Experiments on DAIC-WOZ dataset for binary classification task within depression estimation show advantages of Multi-view architecture over sequential input representations. Our model also outperforms the current state-of-the-art results and provide new SOTA performance on test set of DAIC-WOZ dataset.</abstract>
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%0 Conference Proceedings
%T Analysing relevance of Discourse Structure for Improved Mental Health Estimation
%A Agarwal, Navneet
%A Dias, Gaël
%A Dollfus, Sonia
%Y Yates, Andrew
%Y Desmet, Bart
%Y Prud’hommeaux, Emily
%Y Zirikly, Ayah
%Y Bedrick, Steven
%Y MacAvaney, Sean
%Y Bar, Kfir
%Y Ireland, Molly
%Y Ophir, Yaakov
%S Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F agarwal-etal-2024-analysing
%X Automated depression estimation has received significant research attention in recent years as a result of its growing impact on the global community. Within the context of studies based on patient-therapist interview transcripts, most researchers treat the dyadic discourse as a sequence of unstructured sentences, thus ignoring the discourse structure within the learning process. In this paper we propose Multi-view architectures that divide the input transcript into patient and therapist views based on sentence type in an attempt to utilize symmetric discourse structure for improved model performance. Experiments on DAIC-WOZ dataset for binary classification task within depression estimation show advantages of Multi-view architecture over sequential input representations. Our model also outperforms the current state-of-the-art results and provide new SOTA performance on test set of DAIC-WOZ dataset.
%U https://aclanthology.org/2024.clpsych-1.9
%P 127-132
Markdown (Informal)
[Analysing relevance of Discourse Structure for Improved Mental Health Estimation](https://aclanthology.org/2024.clpsych-1.9) (Agarwal et al., CLPsych-WS 2024)
ACL