@inproceedings{kashif-etal-2026-emotion,
title = "Emotion Recogniton in Conversations - empirical study",
author = "Kashif, Rufaida and
Piwowarski, Benjamin and
Gomez Adorno, Helena",
editor = "Bagdon, Christopher and
Vishnubhotla, Krishnapriya and
Lindquist, Kristen A. and
Ungar, Lyle and
Klinger, Roman and
Mohammad, Saif M.",
booktitle = "Proceedings of Computational Affective Science ({CAS}) @ {LREC} 2026",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.cas-1.8/",
doi = "10.63317/2ju47z39zdo4",
pages = "93--104",
abstract = "Emotion Recognition in Conversations (ERC) requires modeling complex contextual dependencies across dialog turns. While transformer-based models achieve strong performance on ERC benchmarks, several key design choices including context construction, optimization strategies, and imbalance handling remain insufficiently examined. In this work, we conduct a systematic empirical study of transformer-based ERC models across three benchmark datasets. We analyze the impact of context length and directionality, layer freezing, learning rate scheduling, parameter-efficient fine-tuning, and class imbalance mitigation strategies. Our results show that short-to-medium conversational context and moderate layer freezing provide stable and strong performance, while very long context windows, aggressive freezing, and parameter-efficient adaptation offer limited gains. Furthermore, imbalance-aware losses and data augmentation do not consistently outperform standard cross-entropy training. Overall, our findings provide practical insights into effective and stable design choices for transformer-based conversational emotion recognition."
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%0 Conference Proceedings
%T Emotion Recogniton in Conversations - empirical study
%A Kashif, Rufaida
%A Piwowarski, Benjamin
%A Gomez Adorno, Helena
%Y Bagdon, Christopher
%Y Vishnubhotla, Krishnapriya
%Y Lindquist, Kristen A.
%Y Ungar, Lyle
%Y Klinger, Roman
%Y Mohammad, Saif M.
%S Proceedings of Computational Affective Science (CAS) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F kashif-etal-2026-emotion
%X Emotion Recognition in Conversations (ERC) requires modeling complex contextual dependencies across dialog turns. While transformer-based models achieve strong performance on ERC benchmarks, several key design choices including context construction, optimization strategies, and imbalance handling remain insufficiently examined. In this work, we conduct a systematic empirical study of transformer-based ERC models across three benchmark datasets. We analyze the impact of context length and directionality, layer freezing, learning rate scheduling, parameter-efficient fine-tuning, and class imbalance mitigation strategies. Our results show that short-to-medium conversational context and moderate layer freezing provide stable and strong performance, while very long context windows, aggressive freezing, and parameter-efficient adaptation offer limited gains. Furthermore, imbalance-aware losses and data augmentation do not consistently outperform standard cross-entropy training. Overall, our findings provide practical insights into effective and stable design choices for transformer-based conversational emotion recognition.
%R 10.63317/2ju47z39zdo4
%U https://aclanthology.org/2026.cas-1.8/
%U https://doi.org/10.63317/2ju47z39zdo4
%P 93-104
Markdown (Informal)
[Emotion Recogniton in Conversations - empirical study](https://aclanthology.org/2026.cas-1.8/) (Kashif et al., CAS 2026)
ACL
- Rufaida Kashif, Benjamin Piwowarski, and Helena Gomez Adorno. 2026. Emotion Recogniton in Conversations - empirical study. In Proceedings of Computational Affective Science (CAS) @ LREC 2026, pages 93–104, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).