Mitigating Linguistic Artifacts in Emotion Recognition for Conversations from TV Scripts to Daily Conversations

Donovan Ong, Shuo Sun, Jian Su, Bin Chen

Abstract
Emotion Recognition in Conversations (ERC) is a well-studied task with numerous potential real-world applications. However, existing ERC models trained on the MELD dataset derived from TV series, struggle when applied to daily conversation datasets. A closer examination of the datasets unveils the prevalence of linguistic artifacts such as repetitions and interjections in TV scripts, which ERC models may exploit when making predictions. To address this issue, we explore two techniques aimed at reducing the reliance of ERC models on these artifacts: 1) using contrastive learning to prioritize emotional features over dataset-specific linguistic style and 2) refining emotion predictions with pseudo-emotion intensity score. Our experiment results show that reducing reliance on the linguistic style found in TV transcripts could enhance model’s robustness and accuracy in diverse conversational contexts.
Anthology ID:
2024.lrec-main.989
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
11319–11324
Language:
URL:
https://aclanthology.org/2024.lrec-main.989
DOI:
Bibkey:
Cite (ACL):
Donovan Ong, Shuo Sun, Jian Su, and Bin Chen. 2024. Mitigating Linguistic Artifacts in Emotion Recognition for Conversations from TV Scripts to Daily Conversations. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 11319–11324, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Mitigating Linguistic Artifacts in Emotion Recognition for Conversations from TV Scripts to Daily Conversations (Ong et al., LREC-COLING 2024)
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