Academic

Emotion Transcription in Conversation: A Benchmark for Capturing Subtle and Complex Emotional States through Natural Language

arXiv:2603.07138v1 Announce Type: new 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

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Yoshiki Tanaka, Ryuichi Uehara, Koji Inoue, Michimasa Inaba · · 1 min read · 15 views
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arXiv:2603.07138v1 Announce Type: new 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.

Executive Summary

This article presents a novel task, Emotion Transcription in Conversation (ETC), aimed at capturing subtle and complex emotional states through natural language. The authors construct a Japanese dataset comprising text-based dialogues annotated with participants' self-reported emotional states. Baseline models are benchmarked, revealing that current models struggle to infer implicit emotional states. The ETC task has the potential to enhance emotion recognition in conversation and encourage further research in expressive emotion understanding. The publicly available dataset can be used for quantitative analysis and application to emotion recognition. The study's findings and contributions demonstrate a significant step towards improving human-machine interactions.

Key Points

  • ▸ Emotion Transcription in Conversation (ETC) task is proposed to capture subtle and complex emotional states through natural language.
  • ▸ A Japanese dataset is constructed comprising text-based dialogues annotated with participants' self-reported emotional states.
  • ▸ Baseline models struggle to infer implicit emotional states, highlighting the need for further research in expressive emotion understanding.

Merits

Innovative Task Design

The ETC task offers a unique approach to emotion recognition in conversation, focusing on natural language descriptions of emotional states.

Publicly Available Dataset

The dataset is publicly available, enabling researchers to build upon the study's findings and contribute to the development of more expressive emotion understanding.

Demerits

Limited Generalizability

The study's findings may not be generalizable to other languages or cultures, given the dataset's Japanese focus.

Model Limitations

Current models struggle to infer implicit emotional states, highlighting the need for more advanced models and techniques.

Expert Commentary

The article presents a significant contribution to the field of emotion recognition in conversation. The ETC task and dataset offer a unique approach to capturing subtle and complex emotional states, and the study's findings highlight the need for further research in expressive emotion understanding. While the study's limitations, such as the dataset's focus on Japanese and the models' struggle to infer implicit emotional states, are acknowledged, the contributions of this study are substantial. The development of more expressive emotion recognition models has the potential to enhance human-machine interactions in various applications, and the study's implications for policy and guidelines are significant.

Recommendations

  • ✓ Further research should focus on developing more advanced models and techniques to improve the recognition of implicit emotional states.
  • ✓ The ETC task and dataset should be expanded to include other languages and cultures to enhance generalizability and applicability.

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