---
title: "Emotion Transcription in Conversation — Tanaka 2026"
description: "Emotion recognition in dialogue usually assigns fixed categories or valence/arousal scores."
authors: ["Yoshiki Tanaka", "Ryuichi Uehara", "Koji Inoue", "Michimasa Inaba"]
year: 2026
venue: "Proceedings of the Fifteenth Language Resources and Evaluation Conference"
source: "https://aclanthology.org/2026.lrec-1.761/"
doi: "10.63317/29vy7k4asj77"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/tanaka-2026-emotion-transcription-conversation-benchmark-capturing/"
---
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# Emotion Transcription in Conversation: A Benchmark for Capturing Subtle and Complex Emotional States through Natural Language

**Authors:** Yoshiki Tanaka, Ryuichi Uehara, [Koji Inoue](https://md.research.elamur.ai/free/en/authors/koji-inoue/), [Michimasa Inaba](https://md.research.elamur.ai/free/en/authors/michimasa-inaba/) · **Year:** 2026 · **Venue:** Proceedings of the Fifteenth Language Resources and Evaluation Conference · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.lrec-1.761/) · [PDF](https://aclanthology.org/2026.lrec-1.761.pdf) · [DOI 10.63317/29vy7k4asj77](https://doi.org/10.63317/29vy7k4asj77) · [OpenAlex](https://openalex.org/W7160098073)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14)

## Summary
Emotion recognition in dialogue usually assigns fixed categories or valence/arousal scores. These schemes miss blended, subtle or culture-specific feelings. The authors propose a new task, Emotion Transcription in Conversation (ETC), in which a model writes a free-text account of what a speaker feels and intends at each turn. To support the task, they built a Japanese corpus of text chats via the CrowdWorks platform, using a Speaker/Listener setup adapted from EmpatheticDialogues. After every utterance, each participant described their own inner state in their own words. Each description also carries multi-label emotion categories, so the corpus can be used for standard ERC as well. Baselines include GPT-4.1 and Llama-3.1, tested zero-shot, 4-shot and (for Llama) fine-tuned. They are scored with a fine-grained method that splits transcriptions into minimal emotional units and checks entailment against the reference. Fine-tuning improves results, but all models struggle with emotions the speaker leaves unstated. The resource matters because the labels are self-reported rather than LLM-generated, which makes it a step toward richer affect modelling.

## Key points
- **Dataset size:** 1,002 dialogues, each with 5 turns (10 utterances), giving 10,020 utterance–transcription pairs.
- **Lengths:** utterances average about 42.7 characters and emotion transcriptions about 28.9.
- **Prompts and participants:** Speakers recounted a personal episode tied to one of 32 emotion labels, translated from the EmpatheticDialogues set. All crowdworkers' Big Five traits were recorded with TIPI-J.
- **Evaluation:** an LLM breaks each transcription into atomic emotional units, then judges each unit as Supported, Neutral or Not Supported against the reference.
- **Failure cases and availability:** models tend to repeat the surface emotion of the utterance, such as shock, and miss hidden states, such as joy at the partner's engagement. The data is public at github.com/UEC-InabaLab/ETCDataset.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{tanaka2026emotion,
  title = {{Emotion Transcription in Conversation: A Benchmark for Capturing Subtle and Complex Emotional States through Natural Language}},
  author = {Tanaka, Yoshiki and Uehara, Ryuichi and Inoue, Koji and Inaba, Michimasa},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/29vy7k4asj77},
  url = {https://doi.org/10.63317/29vy7k4asj77}
}
```

- [← previous EmoAra: Emotion-Preserving English Speech Transcription and Cross-Lingual Translation with Arabic Text-to-Speech](https://md.research.elamur.ai/free/en/papers/hassan-2026-emoara-emotion-preserving-english-speech/)
- [next → Encoding Logical Relations of Chinese Complex Sentences within the Universal Dependencies Framework](https://md.research.elamur.ai/free/en/papers/zhu-2026-encoding-logical-relations-chinese-complex/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/4/)

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