@inproceedings{yamasaki-etal-2025-multi,
title = "Multi-step or Direct: A Proactive Home-Assistant System Based on Commonsense Reasoning",
author = "Yamasaki, Konosuke and
Tanaka, Shohei and
Yuguchi, Akishige and
Kawano, Seiya and
Yoshino, Koichiro",
editor = "B{\'e}chet, Fr{\'e}d{\'e}ric and
Lef{\`e}vre, Fabrice and
Asher, Nicholas and
Kim, Seokhwan and
Merlin, Teva",
booktitle = "Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = aug,
year = "2025",
address = "Avignon, France",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.sigdial-1.45/",
pages = "561--572",
abstract = "There is a growing expectation for the realization of proactive home-assistant robots that can assist users in their daily lives. It is essential to develop a framework that closely observes the user{'}s surrounding context, selectively extracts relevant information, and infers the user{'}s needs to proactively propose appropriate assistance. In this study, we first extend the Do-I-Demand dataset to define expected proactive assistance actions in domestic situations, where users make ambiguous utterances. These behaviors were defined based on common patterns of support that a majority of users would expect from a robot. We subsequently constructed a framework that infers users' expected assistance actions from ambiguous utterances through commonsense reasoning. We explored two approaches: (1) multi-step reasoning using COMET as a commonsense reasoning engine, and (2) direct reasoning using large language models. Our experimental results suggest that both the multi-step and direct reasoning methods can successfully derive necessary assistance actions even when dealing with ambiguous user utterances."
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<abstract>There is a growing expectation for the realization of proactive home-assistant robots that can assist users in their daily lives. It is essential to develop a framework that closely observes the user’s surrounding context, selectively extracts relevant information, and infers the user’s needs to proactively propose appropriate assistance. In this study, we first extend the Do-I-Demand dataset to define expected proactive assistance actions in domestic situations, where users make ambiguous utterances. These behaviors were defined based on common patterns of support that a majority of users would expect from a robot. We subsequently constructed a framework that infers users’ expected assistance actions from ambiguous utterances through commonsense reasoning. We explored two approaches: (1) multi-step reasoning using COMET as a commonsense reasoning engine, and (2) direct reasoning using large language models. Our experimental results suggest that both the multi-step and direct reasoning methods can successfully derive necessary assistance actions even when dealing with ambiguous user utterances.</abstract>
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%0 Conference Proceedings
%T Multi-step or Direct: A Proactive Home-Assistant System Based on Commonsense Reasoning
%A Yamasaki, Konosuke
%A Tanaka, Shohei
%A Yuguchi, Akishige
%A Kawano, Seiya
%A Yoshino, Koichiro
%Y Béchet, Frédéric
%Y Lefèvre, Fabrice
%Y Asher, Nicholas
%Y Kim, Seokhwan
%Y Merlin, Teva
%S Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2025
%8 August
%I Association for Computational Linguistics
%C Avignon, France
%F yamasaki-etal-2025-multi
%X There is a growing expectation for the realization of proactive home-assistant robots that can assist users in their daily lives. It is essential to develop a framework that closely observes the user’s surrounding context, selectively extracts relevant information, and infers the user’s needs to proactively propose appropriate assistance. In this study, we first extend the Do-I-Demand dataset to define expected proactive assistance actions in domestic situations, where users make ambiguous utterances. These behaviors were defined based on common patterns of support that a majority of users would expect from a robot. We subsequently constructed a framework that infers users’ expected assistance actions from ambiguous utterances through commonsense reasoning. We explored two approaches: (1) multi-step reasoning using COMET as a commonsense reasoning engine, and (2) direct reasoning using large language models. Our experimental results suggest that both the multi-step and direct reasoning methods can successfully derive necessary assistance actions even when dealing with ambiguous user utterances.
%U https://aclanthology.org/2025.sigdial-1.45/
%P 561-572
Markdown (Informal)
[Multi-step or Direct: A Proactive Home-Assistant System Based on Commonsense Reasoning](https://aclanthology.org/2025.sigdial-1.45/) (Yamasaki et al., SIGDIAL 2025)
ACL