Estimating Relationships between Participants in Multi-Party Chat Corpus
Akane Fukushige, Koji Inoue, Keiko Ochi, Tatsuya Kawahara, Sanae Yamashita, Ryuichiro Higashinaka
Correct Metadata for
Abstract
While most existing dialogue studies focus on dyadic (one-on-one) interactions, research on multi-party dialogues has gained increasing importance. One key challenge in multi-party dialogues is identifying and interpreting the relationships between participants. This study focuses on multi-party chat corpus and aims to estimate participant pairs with specific relationships, such as family and acquaintances. We evaluated the performance of large language models (LLMs) in estimating these relationships, comparing them with a logistic regression model that uses interpretable textual features, including the number of turns and the frequency of honorific expressions. The results show that even advanced LLMs struggle with social relationship estimation, performing worse than a simple heuristic-based approach. This finding highlights the need for further improvement in enabling LLMs to naturally capture social relationships in multi-party dialogues.- Anthology ID:
- 2026.iwsds-1.38
- Volume:
- Proceedings of the 16th International Workshop on Spoken Dialogue System Technology
- Month:
- February
- Year:
- 2026
- Address:
- Trento, Italy
- Editors:
- Giuseppe Riccardi, Seyed Mahed Mousavi, Maria Ines Torres, Koichiro Yoshino, Zoraida Callejas, Shammur Absar Chowdhury, Yun-Nung Chen, Frederic Bechet, Joakim Gustafson, Géraldine Damnati, Alex Papangelis, Luis Fernando D’Haro, John Mendonça, Raffaella Bernardi, Dilek Hakkani-Tur, Giuseppe "Pino" Di Fabbrizio, Tatsuya Kawahara, Firoj Alam, Gokhan Tur, Michael Johnston
- Venue:
- IWSDS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 379–390
- Language:
- URL:
- https://aclanthology.org/2026.iwsds-1.38/
- DOI:
- Bibkey:
- Cite (ACL):
- Akane Fukushige, Koji Inoue, Keiko Ochi, Tatsuya Kawahara, Sanae Yamashita, and Ryuichiro Higashinaka. 2026. Estimating Relationships between Participants in Multi-Party Chat Corpus. In Proceedings of the 16th International Workshop on Spoken Dialogue System Technology, pages 379–390, Trento, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Estimating Relationships between Participants in Multi-Party Chat Corpus (Fukushige et al., IWSDS 2026)
- Copy Citation:
- PDF:
- https://aclanthology.org/2026.iwsds-1.38.pdf
Export citation
@inproceedings{fukushige-etal-2026-estimating,
title = "Estimating Relationships between Participants in Multi-Party Chat Corpus",
author = "Fukushige, Akane and
Inoue, Koji and
Ochi, Keiko and
Kawahara, Tatsuya and
Yamashita, Sanae and
Higashinaka, Ryuichiro",
editor = "Riccardi, Giuseppe and
Mousavi, Seyed Mahed and
Torres, Maria Ines and
Yoshino, Koichiro and
Callejas, Zoraida and
Chowdhury, Shammur Absar and
Chen, Yun-Nung and
Bechet, Frederic and
Gustafson, Joakim and
Damnati, G{\'e}raldine and
Papangelis, Alex and
D{'}Haro, Luis Fernando and
Mendon{\c{c}}a, John and
Bernardi, Raffaella and
Hakkani-Tur, Dilek and
Di Fabbrizio, Giuseppe {''}Pino{''} and
Kawahara, Tatsuya and
Alam, Firoj and
Tur, Gokhan and
Johnston, Michael",
booktitle = "Proceedings of the 16th International Workshop on Spoken Dialogue System Technology",
month = feb,
year = "2026",
address = "Trento, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.iwsds-1.38/",
pages = "379--390",
abstract = "While most existing dialogue studies focus on dyadic (one-on-one) interactions, research on multi-party dialogues has gained increasing importance. One key challenge in multi-party dialogues is identifying and interpreting the relationships between participants. This study focuses on multi-party chat corpus and aims to estimate participant pairs with specific relationships, such as family and acquaintances. We evaluated the performance of large language models ({LLM}s) in estimating these relationships, comparing them with a logistic regression model that uses interpretable textual features, including the number of turns and the frequency of honorific expressions. The results show that even advanced {LLM}s struggle with social relationship estimation, performing worse than a simple heuristic-based approach. This finding highlights the need for further improvement in enabling {LLM}s to naturally capture social relationships in multi-party dialogues."
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%0 Conference Proceedings %T Estimating Relationships between Participants in Multi-Party Chat Corpus %A Fukushige, Akane %A Inoue, Koji %A Ochi, Keiko %A Kawahara, Tatsuya %A Yamashita, Sanae %A Higashinaka, Ryuichiro %Y Riccardi, Giuseppe %Y Mousavi, Seyed Mahed %Y Torres, Maria Ines %Y Yoshino, Koichiro %Y Callejas, Zoraida %Y Chowdhury, Shammur Absar %Y Chen, Yun-Nung %Y Bechet, Frederic %Y Gustafson, Joakim %Y Damnati, Géraldine %Y Papangelis, Alex %Y D’Haro, Luis Fernando %Y Mendonça, John %Y Bernardi, Raffaella %Y Hakkani-Tur, Dilek %Y Di Fabbrizio, Giuseppe ”Pino” %Y Kawahara, Tatsuya %Y Alam, Firoj %Y Tur, Gokhan %Y Johnston, Michael %S Proceedings of the 16th International Workshop on Spoken Dialogue System Technology %D 2026 %8 February %I Association for Computational Linguistics %C Trento, Italy %F fukushige-etal-2026-estimating %X While most existing dialogue studies focus on dyadic (one-on-one) interactions, research on multi-party dialogues has gained increasing importance. One key challenge in multi-party dialogues is identifying and interpreting the relationships between participants. This study focuses on multi-party chat corpus and aims to estimate participant pairs with specific relationships, such as family and acquaintances. We evaluated the performance of large language models (LLMs) in estimating these relationships, comparing them with a logistic regression model that uses interpretable textual features, including the number of turns and the frequency of honorific expressions. The results show that even advanced LLMs struggle with social relationship estimation, performing worse than a simple heuristic-based approach. This finding highlights the need for further improvement in enabling LLMs to naturally capture social relationships in multi-party dialogues. %U https://aclanthology.org/2026.iwsds-1.38/ %P 379-390
Markdown (Informal)
[Estimating Relationships between Participants in Multi-Party Chat Corpus](https://aclanthology.org/2026.iwsds-1.38/) (Fukushige et al., IWSDS 2026)
- Estimating Relationships between Participants in Multi-Party Chat Corpus (Fukushige et al., IWSDS 2026)
ACL
- Akane Fukushige, Koji Inoue, Keiko Ochi, Tatsuya Kawahara, Sanae Yamashita, and Ryuichiro Higashinaka. 2026. Estimating Relationships between Participants in Multi-Party Chat Corpus. In Proceedings of the 16th International Workshop on Spoken Dialogue System Technology, pages 379–390, Trento, Italy. Association for Computational Linguistics.