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Dealing with Controversy: An Emotion and Coping Strategy Corpus Based on Role Playing

  • E. Troiano
    ,
  • S. Labat
    ,
  • ,
  • V. Patti
    ,
  • R. Damiano
    ,
  • R. Klinger
  • Vrije University Amsterdam
    ,
  • University of Ghent
    ,
  • University of Torino
    ,
  • Aequa-tech
    ,
  • University of Bamberg
Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-review

Open access

Publication Information

Output type

Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 1634-1658 (23 pages)

Publication milestones

  • Published - 2024

Publication status

Published - 2024

Place of publication

Miami, Florida, USA

Publisher

Association for Computational Linguistics, United States
9798891761681

Publication IDs

  • ORCID: /0000-0001-9337-7250/work/220441839
  • Scopus: 85217618306

Host publication title

Findings of the Association for Computational Linguistics: EMNLP 2024

Abstract

There is a mismatch between psychological and computational studies on emotions. Psychological research aims at explaining and documenting internal mechanisms of these phenomena, while computational work often simplifies
them into labels. Many emotion fundamentals remain under-explored in natural language processing, particularly how emotions develop and how people cope with them. To help reduce this gap, we follow theories on coping, and
treat emotions as strategies to cope with salient situations (i.e., how people deal with emotioneliciting events). This approach allows us to investigate the link between emotions and behavior, which also emerges in language. We
introduce the task of coping identification, together with a corpus to do so, constructed via role-playing. We find that coping strategies realize in text even though they are challenging to recognize, both for humans and automatic
systems trained and prompted on the same task. We thus open up a promising research direction to enhance the capability of models to better capture emotion mechanisms from text.

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Citations
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Captures
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Funding Details

This work has been supported by the Deutsche Forschungsgesellschaft (DFG, Project KL 2869/1-2, No. 380093645). It also received funding from the Flemish Government under the Research Program Artificial Intelligence (grant no. 174E07824) and under the Research Foundation - Flanders (FWO, grant no. 1S96322N). This work was also partially supported by “HARMONIA” project - M4-C2, I1.3 Partenariati Estesi - Cascade Call - FAIR - CUP C63C22000770006 - PE PE0000013 under the NextGenerationEU programme. We further thank Kai Sassenberg for his valuable input and Amit Goldenberg his insightful pointers concerning negativity reframing and re-appraisal.
FundersFunding numbers
DFG
KL 2869/1-2, No. 380093645
RPAI
174E07824
FWO
1S96322N
NextGenerationEU
C63C22000770006 - PE PE0000013