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DeCoDE: Detection of Cognitive Distortion and Emotion Cause Extraction in Clinical Conversations

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Book cover Advances in Information Retrieval (ECIR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13981))

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Abstract

Despite significant evidence linking mental health to almost every major development issue, individuals with mental disorders are among those most at risk of being excluded from development programs. We outline a novel task of detection of Cognitive Distortion and Emotion Cause extraction of associated emotions in conversations. Cognitive distortions are inaccurate thought patterns, beliefs, or perceptions that contribute to negative thinking, which subsequently elevates the chances of several mental illnesses. This work introduces a novel multi-modal mental health conversational corpus manually annotated with emotion, emotion causes, and the presence of cognitive distortion at the utterance level. We propose a multitasking framework that uses multi-modal information as inputs and uses both external commonsense knowledge and factual knowledge from the dataset to learn both tasks at the same time. This is because commonsense knowledge is a key part of understanding how and why emotions are implied. We achieve commendable performance gains on the cognitive distortion detection task (+3.91 F1%) and the emotion cause extraction task (+3 ROS points) when compared to the existing state-of-the-art model.

G. V. Singh and S. Ghosh—These authors contributed equally to this work and are joint first authors.

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Notes

  1. 1.

    https://www.who.int/teams/mental-health-and-substance-use/promotion-prevention/mental-health-in-the-workplace.

  2. 2.

    https://www.iitp.ac.in/~ai-nlp-ml/resources.html#DeCoDE-CoDEC.

  3. 3.

    mental health, psychiatric interview, psychotic, paranoia, hallucination, etc.

  4. 4.

    Links to some sample videos: https://www.youtube.com/watch?v=P7qMfG-yNfA

    https://www.youtube.com/watch?v=Ii2FHbtVJzc.

  5. 5.

    2 Ph.D. linguistics degree holders and 1 undergraduate student from the computer science discipline.

  6. 6.

    https://github.com/audeering/opensmile.

  7. 7.

    https://github.com/kaiqiangh/extracting-video-features-ResNeXt.

  8. 8.

    https://pytorch.org/.

  9. 9.

    https://huggingface.co/docs/transformers/index.

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Author information

Authors and Affiliations

  1. Indian Institute of Technology Patna, Bihta, India

    Gopendra Vikram Singh, Soumitra Ghosh & Asif Ekbal

  2. Indian Institute of Technology Bombay, Mumbai, India

    Pushpak Bhattacharyya

Authors
  1. Gopendra Vikram Singh
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  2. Soumitra Ghosh
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  3. Asif Ekbal
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  4. Pushpak Bhattacharyya
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Corresponding author

Correspondence to Asif Ekbal .

Editor information

Editors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    Jaap Kamps

  2. Université Grenoble-Alpes, Saint-Martin-d’Hères, France

    Lorraine Goeuriot

  3. Università della Svizzera Italiana, Lugano, Switzerland

    Fabio Crestani

  4. University of Copenhagen, Copenhagen, Denmark

    Maria Maistro

  5. University of Tsukuba, Ibaraki, Japan

    Hideo Joho

  6. Dublin City University, Dublin, Ireland

    Brian Davis

  7. Dublin City University, Dublin, Ireland

    Cathal Gurrin

  8. Universität Regensburg, Regensburg, Germany

    Udo Kruschwitz

  9. Dublin City University, Dublin, Ireland

    Annalina Caputo

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This study has been evaluated and approved by our Institutional Review Board (IRB). The videos used to create the dataset for this study do not have any copyright clauses attached to them. Furthermore, the videos are shared via various channels for the main purpose of facilitating research and educational purposes.

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Singh, G.V., Ghosh, S., Ekbal, A., Bhattacharyya, P. (2023). DeCoDE: Detection of Cognitive Distortion and Emotion Cause Extraction in Clinical Conversations. In: Kamps, J., et al. Advances in Information Retrieval. ECIR 2023. Lecture Notes in Computer Science, vol 13981. Springer, Cham. https://doi.org/10.1007/978-3-031-28238-6_11

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  • DOI: https://doi.org/10.1007/978-3-031-28238-6_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28237-9

  • Online ISBN: 978-3-031-28238-6

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