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    <abstract>Emotion Recognition in Conversation (ERC) is critical for enabling natural human-machine interactions. However, existing methods predominantly employ categorical or dimensional emotion annotations, which often fail to adequately represent complex, subtle, or culturally specific emotional nuances. To overcome this limitation, we propose a novel task named Emotion Transcription in Conversation (ETC). This task focuses on generating natural language descriptions that accurately reflect speakers’ emotional states within conversational contexts. To address the ETC, we constructed a Japanese dataset comprising text-based dialogues annotated with participants’ self-reported emotional states, described in natural language. The dataset also includes emotion category labels for each transcription, enabling quantitative analysis and its application to ERC. We benchmarked baseline models, finding that while fine-tuning on our dataset enhances model performance, current models still struggle to infer implicit emotional states. The ETC task will encourage further research into more expressive emotion understanding in dialogue. The dataset is publicly available at https://github.com/UEC-InabaLab/ETCDataset.</abstract>
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%0 Conference Proceedings
%T Emotion Transcription in Conversation: A Benchmark for Capturing Subtle and Complex Emotional States through Natural Language
%A Tanaka, Yoshiki
%A Uehara, Ryuichi
%A Inoue, Koji
%A Inaba, Michimasa
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F tanaka-etal-2026-emotion
%X Emotion Recognition in Conversation (ERC) is critical for enabling natural human-machine interactions. However, existing methods predominantly employ categorical or dimensional emotion annotations, which often fail to adequately represent complex, subtle, or culturally specific emotional nuances. To overcome this limitation, we propose a novel task named Emotion Transcription in Conversation (ETC). This task focuses on generating natural language descriptions that accurately reflect speakers’ emotional states within conversational contexts. To address the ETC, we constructed a Japanese dataset comprising text-based dialogues annotated with participants’ self-reported emotional states, described in natural language. The dataset also includes emotion category labels for each transcription, enabling quantitative analysis and its application to ERC. We benchmarked baseline models, finding that while fine-tuning on our dataset enhances model performance, current models still struggle to infer implicit emotional states. The ETC task will encourage further research into more expressive emotion understanding in dialogue. The dataset is publicly available at https://github.com/UEC-InabaLab/ETCDataset.
%R 10.63317/29vy7k4asj77
%U https://aclanthology.org/2026.lrec-1.761/
%U https://doi.org/10.63317/29vy7k4asj77
%P 9692-9709

Markdown (Informal)

[Emotion Transcription in Conversation: A Benchmark for Capturing Subtle and Complex Emotional States through Natural Language](https://aclanthology.org/2026.lrec-1.761/) (Tanaka et al., LREC 2026)

• Emotion Transcription in Conversation: A Benchmark for Capturing Subtle and Complex Emotional States through Natural Language (Tanaka et al., LREC 2026)

ACL

• Yoshiki Tanaka, Ryuichi Uehara, Koji Inoue, and Michimasa Inaba. 2026. Emotion Transcription in Conversation: A Benchmark for Capturing Subtle and Complex Emotional States through Natural Language. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 9692–9709, Palma de Mallorca, Spain. ELRA Language Resource Association.