@inproceedings{zhao-etal-2023-others,
title = "{``}What do others think?{''}: Task-Oriented Conversational Modeling with Subjective Knowledge",
author = "Zhao, Chao and
Gella, Spandana and
Kim, Seokhwan and
Jin, Di and
Hazarika, Devamanyu and
Papangelis, Alexandros and
Hedayatnia, Behnam and
Namazifar, Mahdi and
Liu, Yang and
Hakkani-Tur, Dilek",
editor = "Stoyanchev, Svetlana and
Joty, Shafiq and
Schlangen, David and
Dusek, Ondrej and
Kennington, Casey and
Alikhani, Malihe",
booktitle = "Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = sep,
year = "2023",
address = "Prague, Czechia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.sigdial-1.28",
doi = "10.18653/v1/2023.sigdial-1.28",
pages = "309--323",
abstract = "Task-oriented Dialogue (TOD) Systems aim to build dialogue systems that assist users in accomplishing specific goals, such as booking a hotel or a restaurant. Traditional TODs rely on domain-specific APIs/DBs or external factual knowledge to generate responses, which cannot accommodate subjective user requests (e.g.,{''}Is the WIFI reliable?{''} or {``}Does the restaurant have a good atmosphere?{''}). To address this issue, we propose a novel task of subjective-knowledge-based TOD (SK-TOD). We also propose the first corresponding dataset, which contains subjective knowledge-seeking dialogue contexts and manually annotated responses grounded in subjective knowledge sources. When evaluated with existing TOD approaches, we find that this task poses new challenges such as aggregating diverse opinions from multiple knowledge snippets. We hope this task and dataset can promote further research on TOD and subjective content understanding. The code and the dataset are available at https://github.com/alexa/dstc11-track5.",
}
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<abstract>Task-oriented Dialogue (TOD) Systems aim to build dialogue systems that assist users in accomplishing specific goals, such as booking a hotel or a restaurant. Traditional TODs rely on domain-specific APIs/DBs or external factual knowledge to generate responses, which cannot accommodate subjective user requests (e.g.,”Is the WIFI reliable?” or “Does the restaurant have a good atmosphere?”). To address this issue, we propose a novel task of subjective-knowledge-based TOD (SK-TOD). We also propose the first corresponding dataset, which contains subjective knowledge-seeking dialogue contexts and manually annotated responses grounded in subjective knowledge sources. When evaluated with existing TOD approaches, we find that this task poses new challenges such as aggregating diverse opinions from multiple knowledge snippets. We hope this task and dataset can promote further research on TOD and subjective content understanding. The code and the dataset are available at https://github.com/alexa/dstc11-track5.</abstract>
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%0 Conference Proceedings
%T “What do others think?”: Task-Oriented Conversational Modeling with Subjective Knowledge
%A Zhao, Chao
%A Gella, Spandana
%A Kim, Seokhwan
%A Jin, Di
%A Hazarika, Devamanyu
%A Papangelis, Alexandros
%A Hedayatnia, Behnam
%A Namazifar, Mahdi
%A Liu, Yang
%A Hakkani-Tur, Dilek
%Y Stoyanchev, Svetlana
%Y Joty, Shafiq
%Y Schlangen, David
%Y Dusek, Ondrej
%Y Kennington, Casey
%Y Alikhani, Malihe
%S Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2023
%8 September
%I Association for Computational Linguistics
%C Prague, Czechia
%F zhao-etal-2023-others
%X Task-oriented Dialogue (TOD) Systems aim to build dialogue systems that assist users in accomplishing specific goals, such as booking a hotel or a restaurant. Traditional TODs rely on domain-specific APIs/DBs or external factual knowledge to generate responses, which cannot accommodate subjective user requests (e.g.,”Is the WIFI reliable?” or “Does the restaurant have a good atmosphere?”). To address this issue, we propose a novel task of subjective-knowledge-based TOD (SK-TOD). We also propose the first corresponding dataset, which contains subjective knowledge-seeking dialogue contexts and manually annotated responses grounded in subjective knowledge sources. When evaluated with existing TOD approaches, we find that this task poses new challenges such as aggregating diverse opinions from multiple knowledge snippets. We hope this task and dataset can promote further research on TOD and subjective content understanding. The code and the dataset are available at https://github.com/alexa/dstc11-track5.
%R 10.18653/v1/2023.sigdial-1.28
%U https://aclanthology.org/2023.sigdial-1.28
%U https://doi.org/10.18653/v1/2023.sigdial-1.28
%P 309-323
Markdown (Informal)
[“What do others think?”: Task-Oriented Conversational Modeling with Subjective Knowledge](https://aclanthology.org/2023.sigdial-1.28) (Zhao et al., SIGDIAL 2023)
ACL
- Chao Zhao, Spandana Gella, Seokhwan Kim, Di Jin, Devamanyu Hazarika, Alexandros Papangelis, Behnam Hedayatnia, Mahdi Namazifar, Yang Liu, and Dilek Hakkani-Tur. 2023. “What do others think?”: Task-Oriented Conversational Modeling with Subjective Knowledge. In Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 309–323, Prague, Czechia. Association for Computational Linguistics.