@inproceedings{arya-etal-2023-bootstrapping,
title = "Bootstrapping a Conversational Guide for Colonoscopy Prep",
author = "Arya, Pulkit and
Bloomquist, Madeleine and
Chakraborty, Subhankar and
Perrault, Andrew and
Schuler, William and
Fosler-Lussier, Eric and
White, Michael",
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-1.38",
doi = "10.18653/v1/2023.sigdial-1.38",
pages = "413--420",
abstract = "Creating conversational systems for niche domains is a challenging task, further exacerbated by a lack of quality datasets. We explore the construction of safer conversational systems for guiding patients in preparing for colonoscopies. This has required a data generation pipeline to generate a minimum viable dataset to bootstrap a semantic parser, augmented by automatic paraphrasing. Our study suggests large language models (e.g., GPT-3.5 and GPT-4) are a viable alternative to crowd sourced paraphrasing, but conversational systems that rely upon language models{'} ability to do temporal reasoning struggle to provide accurate responses. A neural-symbolic system that performs temporal reasoning on an intermediate representation of user queries shows promising results compared to an end-to-end dialogue system, improving the number of correct responses while vastly reducing the number of incorrect or misleading ones.",
}
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<abstract>Creating conversational systems for niche domains is a challenging task, further exacerbated by a lack of quality datasets. We explore the construction of safer conversational systems for guiding patients in preparing for colonoscopies. This has required a data generation pipeline to generate a minimum viable dataset to bootstrap a semantic parser, augmented by automatic paraphrasing. Our study suggests large language models (e.g., GPT-3.5 and GPT-4) are a viable alternative to crowd sourced paraphrasing, but conversational systems that rely upon language models’ ability to do temporal reasoning struggle to provide accurate responses. A neural-symbolic system that performs temporal reasoning on an intermediate representation of user queries shows promising results compared to an end-to-end dialogue system, improving the number of correct responses while vastly reducing the number of incorrect or misleading ones.</abstract>
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%0 Conference Proceedings
%T Bootstrapping a Conversational Guide for Colonoscopy Prep
%A Arya, Pulkit
%A Bloomquist, Madeleine
%A Chakraborty, Subhankar
%A Perrault, Andrew
%A Schuler, William
%A Fosler-Lussier, Eric
%A White, Michael
%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 arya-etal-2023-bootstrapping
%X Creating conversational systems for niche domains is a challenging task, further exacerbated by a lack of quality datasets. We explore the construction of safer conversational systems for guiding patients in preparing for colonoscopies. This has required a data generation pipeline to generate a minimum viable dataset to bootstrap a semantic parser, augmented by automatic paraphrasing. Our study suggests large language models (e.g., GPT-3.5 and GPT-4) are a viable alternative to crowd sourced paraphrasing, but conversational systems that rely upon language models’ ability to do temporal reasoning struggle to provide accurate responses. A neural-symbolic system that performs temporal reasoning on an intermediate representation of user queries shows promising results compared to an end-to-end dialogue system, improving the number of correct responses while vastly reducing the number of incorrect or misleading ones.
%R 10.18653/v1/2023.sigdial-1.38
%U https://aclanthology.org/2023.sigdial-1.38
%U https://doi.org/10.18653/v1/2023.sigdial-1.38
%P 413-420
Markdown (Informal)
[Bootstrapping a Conversational Guide for Colonoscopy Prep](https://aclanthology.org/2023.sigdial-1.38) (Arya et al., SIGDIAL 2023)
ACL
- Pulkit Arya, Madeleine Bloomquist, Subhankar Chakraborty, Andrew Perrault, William Schuler, Eric Fosler-Lussier, and Michael White. 2023. Bootstrapping a Conversational Guide for Colonoscopy Prep. In Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 413–420, Prague, Czechia. Association for Computational Linguistics.