@inproceedings{acikgoz-etal-2026-mac,
title = "{MAC}: A Multi-Agent Framework for Interactive User Clarification in Multi-turn Conversations",
author = "Acikgoz, Emre Can and
Oh, Jinoh and
Jeon, Joo Hyuk and
Hao, Jie and
Ji, Heng and
Hakkani-Tur, Dilek and
Tur, Gokhan and
Li, Xiang and
Ma, Chengyuan and
Fan, Xing",
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-1.1/",
pages = "1--17",
abstract = "Conversational agents often encounter ambiguous user requests, requiring an effective clarification to successfully complete tasks. While recent advancements in real-world applications favor multi-agent architectures to manage complex conversational scenarios efficiently, ambiguity resolution remains a critical and underexplored challenge{---}particularly due to the difficulty of determining which agent should initiate a clarification and how agents should coordinate their actions when faced with uncertain or incomplete user input. The fundamental questions of when to interrupt a user and how to formulate the optimal clarification query within the most optimal multi-agent settings remain open. In this paper, we propose {MAC} (Multi-Agent Clarification), an interactive multi-agent framework specifically optimized to resolve user ambiguities by strategically managing clarification dialogues. We first introduce a novel taxonomy categorizing user ambiguities to systematically guide clarification strategies. Then, we present {MAC} that autonomously coordinates multiple agents to interact synergistically with users. Empirical evaluations on {M}ulti{WOZ} 2.4 demonstrate that enabling clarification at both levels increases task success rate 7.8{\%} (54.5 {\textrightarrow} 62.3) and reduces the average number of dialogue turns (6.53 {\textrightarrow} 4.86) by eliciting all required user information up front and minimizing repetition. Our findings highlight the importance of active user interaction and role-aware clarification for more reliable human{--}agent communication."
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<abstract>Conversational agents often encounter ambiguous user requests, requiring an effective clarification to successfully complete tasks. While recent advancements in real-world applications favor multi-agent architectures to manage complex conversational scenarios efficiently, ambiguity resolution remains a critical and underexplored challenge—particularly due to the difficulty of determining which agent should initiate a clarification and how agents should coordinate their actions when faced with uncertain or incomplete user input. The fundamental questions of when to interrupt a user and how to formulate the optimal clarification query within the most optimal multi-agent settings remain open. In this paper, we propose MAC (Multi-Agent Clarification), an interactive multi-agent framework specifically optimized to resolve user ambiguities by strategically managing clarification dialogues. We first introduce a novel taxonomy categorizing user ambiguities to systematically guide clarification strategies. Then, we present MAC that autonomously coordinates multiple agents to interact synergistically with users. Empirical evaluations on MultiWOZ 2.4 demonstrate that enabling clarification at both levels increases task success rate 7.8% (54.5 → 62.3) and reduces the average number of dialogue turns (6.53 → 4.86) by eliciting all required user information up front and minimizing repetition. Our findings highlight the importance of active user interaction and role-aware clarification for more reliable human–agent communication.</abstract>
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%0 Conference Proceedings
%T MAC: A Multi-Agent Framework for Interactive User Clarification in Multi-turn Conversations
%A Acikgoz, Emre Can
%A Oh, Jinoh
%A Jeon, Joo Hyuk
%A Hao, Jie
%A Ji, Heng
%A Hakkani-Tur, Dilek
%A Tur, Gokhan
%A Li, Xiang
%A Ma, Chengyuan
%A Fan, Xing
%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 acikgoz-etal-2026-mac
%X Conversational agents often encounter ambiguous user requests, requiring an effective clarification to successfully complete tasks. While recent advancements in real-world applications favor multi-agent architectures to manage complex conversational scenarios efficiently, ambiguity resolution remains a critical and underexplored challenge—particularly due to the difficulty of determining which agent should initiate a clarification and how agents should coordinate their actions when faced with uncertain or incomplete user input. The fundamental questions of when to interrupt a user and how to formulate the optimal clarification query within the most optimal multi-agent settings remain open. In this paper, we propose MAC (Multi-Agent Clarification), an interactive multi-agent framework specifically optimized to resolve user ambiguities by strategically managing clarification dialogues. We first introduce a novel taxonomy categorizing user ambiguities to systematically guide clarification strategies. Then, we present MAC that autonomously coordinates multiple agents to interact synergistically with users. Empirical evaluations on MultiWOZ 2.4 demonstrate that enabling clarification at both levels increases task success rate 7.8% (54.5 → 62.3) and reduces the average number of dialogue turns (6.53 → 4.86) by eliciting all required user information up front and minimizing repetition. Our findings highlight the importance of active user interaction and role-aware clarification for more reliable human–agent communication.
%U https://aclanthology.org/2026.iwsds-1.1/
%P 1-17
Markdown (Informal)
[MAC: A Multi-Agent Framework for Interactive User Clarification in Multi-turn Conversations](https://aclanthology.org/2026.iwsds-1.1/) (Acikgoz et al., IWSDS 2026)
ACL
- Emre Can Acikgoz, Jinoh Oh, Joo Hyuk Jeon, Jie Hao, Heng Ji, Dilek Hakkani-Tur, Gokhan Tur, Xiang Li, Chengyuan Ma, and Xing Fan. 2026. MAC: A Multi-Agent Framework for Interactive User Clarification in Multi-turn Conversations. In Proceedings of the 16th International Workshop on Spoken Dialogue System Technology, pages 1–17, Trento, Italy. Association for Computational Linguistics.