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From Felt Sense to Words: Construction and Analysis of a Focusing Dialogue Dataset for Verbalization

Summary

Some people struggle to put their inner states into words, even though doing so can improve self-understanding and well-being. Focusing-Oriented Psychotherapy helps by directing a person's attention to their "felt sense," a bodily feeling that has not yet been put into words. A trained listener then helps the person find fitting words for it. Trained listeners are scarce, so the authors aim to build automated Focusing dialogue systems. As a first step, they recorded a multimodal dataset in Japanese: Zoom sessions between professional listeners and focusers, with audio and video. Each session carries:

  • listener ratings of verbalization progress (EXP checklist),
  • the focusers' prior Focusing attitudes (FMS-18),
  • post-session evaluations (J-SEQ),
  • dialogue act labels.

Their analysis shows that more verbalization progress does not automatically mean focusers rate the session more positively. Attention to one's felt sense, however, is associated with greater progress. Listeners also adjust their style: they ask more questions when progress or attitude is low and give more responsive acknowledgments when it is high. These results suggest that systems should balance progress against user satisfaction and adapt their strategy to each user.

Key points

  • The dataset contains 50 Focusing dialogues (762 minutes, 17,986 utterances), all in Japanese and all built on a single Focusing work. Participants sat in separate soundproof rooms, and the sessions were recorded as audio and video.
  • Annotations per dialogue:
    • EXP score from a 17-item checklist, with levels from 1 (Very Low) to 5 (Very High);
    • FMS-18 with three factors: attention, distance and acceptance;
    • J-SEQ subjective ratings from both participants;
    • utterance-level DA tags borrowed from attentive-listening research.
  • Dialogues were split into high and low FMS groups at the mean total FMS-18 score of 53.16.
    • With low-FMS focusers, listeners used QUES more and the QUES→DISC pair was more common.
    • With high-FMS focusers, listeners used more PROP/ACK and ACK→DISC.
  • Listener DA distributions differ markedly from those in attentive listening datasets. Strategies from attentive-listening systems, such as listener self-disclosure, therefore do not transfer directly to Focusing.
  • Limitations:
    • The DA tag set is not tailored to Focusing.
    • Non-verbal cues were not analyzed.
    • The design implications have not been tested in an LLM-based system.
    • The dataset is not yet public; a release is being prepared.

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

Topics: Dialogue systems

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.sigdial-1.19

@inproceedings{tsunomori2026felt,
  title = {{From Felt Sense to Words: Construction and Analysis of a Focusing Dialogue Dataset for Verbalization}},
  author = {Tsunomori, Yuiko and Chiba, Yuya and Koshikawa, Yosuke},
  booktitle = {Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue},
  year = {2026},
  doi = {10.18653/v1/2026.sigdial-1.19},
  url = {https://doi.org/10.18653/v1/2026.sigdial-1.19}
}

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