@inproceedings{yoshida-yoshino-2026-analyzing,
title = "Analyzing Utterance Selection for Unnoticeable Topic Induction in Target-Guided Conversation Systems",
author = "Yoshida, Kai and
Yoshino, Koichiro",
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-1.12/",
pages = "114--122",
abstract = "Target-guided conversation systems conduct dialogues to achieve predefined conversation targets, such as recommending target goods or talking about target topics. In such systems, it is important to transition topics naturally toward the target without letting the user notice the intention behind the topic induction. In this study, we implement a surprisal-based framework that quantifies the sense of induction, target awareness, and naturalness of system utterances by computing surprisal using an external language model. Experimental results from dialogue sessions demonstrate that utterance selection based on the proposed surprisal-based evaluation reduces the perceived induction of system utterances. Furthermore, correlation analysis reveals that the proposed metric aligns with human perception of induction. We also observe that surprisal values with respect to the target gradually decrease as the conversation progresses, indicating that the model implicitly learns to approach the target more naturally over time."
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<abstract>Target-guided conversation systems conduct dialogues to achieve predefined conversation targets, such as recommending target goods or talking about target topics. In such systems, it is important to transition topics naturally toward the target without letting the user notice the intention behind the topic induction. In this study, we implement a surprisal-based framework that quantifies the sense of induction, target awareness, and naturalness of system utterances by computing surprisal using an external language model. Experimental results from dialogue sessions demonstrate that utterance selection based on the proposed surprisal-based evaluation reduces the perceived induction of system utterances. Furthermore, correlation analysis reveals that the proposed metric aligns with human perception of induction. We also observe that surprisal values with respect to the target gradually decrease as the conversation progresses, indicating that the model implicitly learns to approach the target more naturally over time.</abstract>
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%0 Conference Proceedings
%T Analyzing Utterance Selection for Unnoticeable Topic Induction in Target-Guided Conversation Systems
%A Yoshida, Kai
%A Yoshino, Koichiro
%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 yoshida-yoshino-2026-analyzing
%X Target-guided conversation systems conduct dialogues to achieve predefined conversation targets, such as recommending target goods or talking about target topics. In such systems, it is important to transition topics naturally toward the target without letting the user notice the intention behind the topic induction. In this study, we implement a surprisal-based framework that quantifies the sense of induction, target awareness, and naturalness of system utterances by computing surprisal using an external language model. Experimental results from dialogue sessions demonstrate that utterance selection based on the proposed surprisal-based evaluation reduces the perceived induction of system utterances. Furthermore, correlation analysis reveals that the proposed metric aligns with human perception of induction. We also observe that surprisal values with respect to the target gradually decrease as the conversation progresses, indicating that the model implicitly learns to approach the target more naturally over time.
%U https://aclanthology.org/2026.iwsds-1.12/
%P 114-122
Markdown (Informal)
[Analyzing Utterance Selection for Unnoticeable Topic Induction in Target-Guided Conversation Systems](https://aclanthology.org/2026.iwsds-1.12/) (Yoshida & Yoshino, IWSDS 2026)
ACL