@inproceedings{choi-etal-2022-wizard,
title = "Wizard of Tasks: A Novel Conversational Dataset for Solving Real-World Tasks in Conversational Settings",
author = "Choi, Jason Ingyu and
Kuzi, Saar and
Vedula, Nikhita and
Zhao, Jie and
Castellucci, Giuseppe and
Collins, Marcus and
Malmasi, Shervin and
Rokhlenko, Oleg and
Agichtein, Eugene",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.310",
pages = "3514--3529",
abstract = "Conversational Task Assistants (CTAs) are conversational agents whose goal is to help humans perform real-world tasks. CTAs can help in exploring available tasks, answering task-specific questions and guiding users through step-by-step instructions. In this work, we present Wizard of Tasks, the first corpus of such conversations in two domains: Cooking and Home Improvement. We crowd-sourced a total of 549 conversations (18,077 utterances) with an asynchronous Wizard-of-Oz setup, relying on recipes from WholeFoods Market for the cooking domain, and WikiHow articles for the home improvement domain. We present a detailed data analysis and show that the collected data can be a valuable and challenging resource for CTAs in two tasks: Intent Classification (IC) and Abstractive Question Answering (AQA). While on IC we acquired a high performing model ({\textgreater}85{\%} F1), on AQA the performance is far from being satisfactory ({\textasciitilde}27{\%} BertScore-F1), suggesting that more work is needed to solve the task of low-resource AQA.",
}
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<abstract>Conversational Task Assistants (CTAs) are conversational agents whose goal is to help humans perform real-world tasks. CTAs can help in exploring available tasks, answering task-specific questions and guiding users through step-by-step instructions. In this work, we present Wizard of Tasks, the first corpus of such conversations in two domains: Cooking and Home Improvement. We crowd-sourced a total of 549 conversations (18,077 utterances) with an asynchronous Wizard-of-Oz setup, relying on recipes from WholeFoods Market for the cooking domain, and WikiHow articles for the home improvement domain. We present a detailed data analysis and show that the collected data can be a valuable and challenging resource for CTAs in two tasks: Intent Classification (IC) and Abstractive Question Answering (AQA). While on IC we acquired a high performing model (\textgreater85% F1), on AQA the performance is far from being satisfactory (~27% BertScore-F1), suggesting that more work is needed to solve the task of low-resource AQA.</abstract>
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%0 Conference Proceedings
%T Wizard of Tasks: A Novel Conversational Dataset for Solving Real-World Tasks in Conversational Settings
%A Choi, Jason Ingyu
%A Kuzi, Saar
%A Vedula, Nikhita
%A Zhao, Jie
%A Castellucci, Giuseppe
%A Collins, Marcus
%A Malmasi, Shervin
%A Rokhlenko, Oleg
%A Agichtein, Eugene
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F choi-etal-2022-wizard
%X Conversational Task Assistants (CTAs) are conversational agents whose goal is to help humans perform real-world tasks. CTAs can help in exploring available tasks, answering task-specific questions and guiding users through step-by-step instructions. In this work, we present Wizard of Tasks, the first corpus of such conversations in two domains: Cooking and Home Improvement. We crowd-sourced a total of 549 conversations (18,077 utterances) with an asynchronous Wizard-of-Oz setup, relying on recipes from WholeFoods Market for the cooking domain, and WikiHow articles for the home improvement domain. We present a detailed data analysis and show that the collected data can be a valuable and challenging resource for CTAs in two tasks: Intent Classification (IC) and Abstractive Question Answering (AQA). While on IC we acquired a high performing model (\textgreater85% F1), on AQA the performance is far from being satisfactory (~27% BertScore-F1), suggesting that more work is needed to solve the task of low-resource AQA.
%U https://aclanthology.org/2022.coling-1.310
%P 3514-3529
Markdown (Informal)
[Wizard of Tasks: A Novel Conversational Dataset for Solving Real-World Tasks in Conversational Settings](https://aclanthology.org/2022.coling-1.310) (Choi et al., COLING 2022)
ACL
- Jason Ingyu Choi, Saar Kuzi, Nikhita Vedula, Jie Zhao, Giuseppe Castellucci, Marcus Collins, Shervin Malmasi, Oleg Rokhlenko, and Eugene Agichtein. 2022. Wizard of Tasks: A Novel Conversational Dataset for Solving Real-World Tasks in Conversational Settings. In Proceedings of the 29th International Conference on Computational Linguistics, pages 3514–3529, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.