@inproceedings{juhng-etal-2023-discourse,
title = "Discourse-Level Representations can Improve Prediction of Degree of Anxiety",
author = "Juhng, Swanie and
Matero, Matthew and
Varadarajan, Vasudha and
Eichstaedt, Johannes and
V Ganesan, Adithya and
Schwartz, H. Andrew",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.128",
doi = "10.18653/v1/2023.acl-short.128",
pages = "1500--1511",
abstract = "Anxiety disorders are the most common of mental illnesses, but relatively little is known about how to detect them from language. The primary clinical manifestation of anxiety is worry associated cognitive distortions, which are likely expressed at the discourse-level of semantics. Here, we investigate the development of a modern linguistic assessment for degree of anxiety, specifically evaluating the utility of discourse-level information in addition to lexical-level large language model embeddings. We find that a combined lexico-discourse model outperforms models based solely on state-of-the-art contextual embeddings (RoBERTa), with discourse-level representations derived from Sentence-BERT and DiscRE both providing additional predictive power not captured by lexical-level representations. Interpreting the model, we find that discourse patterns of causal explanations, among others, were used significantly more by those scoring high in anxiety, dovetailing with psychological literature.",
}
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<abstract>Anxiety disorders are the most common of mental illnesses, but relatively little is known about how to detect them from language. The primary clinical manifestation of anxiety is worry associated cognitive distortions, which are likely expressed at the discourse-level of semantics. Here, we investigate the development of a modern linguistic assessment for degree of anxiety, specifically evaluating the utility of discourse-level information in addition to lexical-level large language model embeddings. We find that a combined lexico-discourse model outperforms models based solely on state-of-the-art contextual embeddings (RoBERTa), with discourse-level representations derived from Sentence-BERT and DiscRE both providing additional predictive power not captured by lexical-level representations. Interpreting the model, we find that discourse patterns of causal explanations, among others, were used significantly more by those scoring high in anxiety, dovetailing with psychological literature.</abstract>
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%0 Conference Proceedings
%T Discourse-Level Representations can Improve Prediction of Degree of Anxiety
%A Juhng, Swanie
%A Matero, Matthew
%A Varadarajan, Vasudha
%A Eichstaedt, Johannes
%A V Ganesan, Adithya
%A Schwartz, H. Andrew
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F juhng-etal-2023-discourse
%X Anxiety disorders are the most common of mental illnesses, but relatively little is known about how to detect them from language. The primary clinical manifestation of anxiety is worry associated cognitive distortions, which are likely expressed at the discourse-level of semantics. Here, we investigate the development of a modern linguistic assessment for degree of anxiety, specifically evaluating the utility of discourse-level information in addition to lexical-level large language model embeddings. We find that a combined lexico-discourse model outperforms models based solely on state-of-the-art contextual embeddings (RoBERTa), with discourse-level representations derived from Sentence-BERT and DiscRE both providing additional predictive power not captured by lexical-level representations. Interpreting the model, we find that discourse patterns of causal explanations, among others, were used significantly more by those scoring high in anxiety, dovetailing with psychological literature.
%R 10.18653/v1/2023.acl-short.128
%U https://aclanthology.org/2023.acl-short.128
%U https://doi.org/10.18653/v1/2023.acl-short.128
%P 1500-1511
Markdown (Informal)
[Discourse-Level Representations can Improve Prediction of Degree of Anxiety](https://aclanthology.org/2023.acl-short.128) (Juhng et al., ACL 2023)
ACL
- Swanie Juhng, Matthew Matero, Vasudha Varadarajan, Johannes Eichstaedt, Adithya V Ganesan, and H. Andrew Schwartz. 2023. Discourse-Level Representations can Improve Prediction of Degree of Anxiety. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 1500–1511, Toronto, Canada. Association for Computational Linguistics.