@inproceedings{lyu-etal-2021-goal,
title = "Goal-Oriented Script Construction",
author = "Lyu, Qing and
Zhang, Li and
Callison-Burch, Chris",
editor = "Belz, Anya and
Fan, Angela and
Reiter, Ehud and
Sripada, Yaji",
booktitle = "Proceedings of the 14th International Conference on Natural Language Generation",
month = aug,
year = "2021",
address = "Aberdeen, Scotland, UK",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.inlg-1.19",
doi = "10.18653/v1/2021.inlg-1.19",
pages = "184--200",
abstract = "The knowledge of scripts, common chains of events in stereotypical scenarios, is a valuable asset for task-oriented natural language understanding systems. We propose the Goal-Oriented Script Construction task, where a model produces a sequence of steps to accomplish a given goal. We pilot our task on the first multilingual script learning dataset supporting 18 languages collected from wikiHow, a website containing half a million how-to articles. For baselines, we consider both a generation-based approach using a language model and a retrieval-based approach by first retrieving the relevant steps from a large candidate pool and then ordering them. We show that our task is practical, feasible but challenging for state-of-the-art Transformer models, and that our methods can be readily deployed for various other datasets and domains with decent zero-shot performance.",
}
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<abstract>The knowledge of scripts, common chains of events in stereotypical scenarios, is a valuable asset for task-oriented natural language understanding systems. We propose the Goal-Oriented Script Construction task, where a model produces a sequence of steps to accomplish a given goal. We pilot our task on the first multilingual script learning dataset supporting 18 languages collected from wikiHow, a website containing half a million how-to articles. For baselines, we consider both a generation-based approach using a language model and a retrieval-based approach by first retrieving the relevant steps from a large candidate pool and then ordering them. We show that our task is practical, feasible but challenging for state-of-the-art Transformer models, and that our methods can be readily deployed for various other datasets and domains with decent zero-shot performance.</abstract>
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%0 Conference Proceedings
%T Goal-Oriented Script Construction
%A Lyu, Qing
%A Zhang, Li
%A Callison-Burch, Chris
%Y Belz, Anya
%Y Fan, Angela
%Y Reiter, Ehud
%Y Sripada, Yaji
%S Proceedings of the 14th International Conference on Natural Language Generation
%D 2021
%8 August
%I Association for Computational Linguistics
%C Aberdeen, Scotland, UK
%F lyu-etal-2021-goal
%X The knowledge of scripts, common chains of events in stereotypical scenarios, is a valuable asset for task-oriented natural language understanding systems. We propose the Goal-Oriented Script Construction task, where a model produces a sequence of steps to accomplish a given goal. We pilot our task on the first multilingual script learning dataset supporting 18 languages collected from wikiHow, a website containing half a million how-to articles. For baselines, we consider both a generation-based approach using a language model and a retrieval-based approach by first retrieving the relevant steps from a large candidate pool and then ordering them. We show that our task is practical, feasible but challenging for state-of-the-art Transformer models, and that our methods can be readily deployed for various other datasets and domains with decent zero-shot performance.
%R 10.18653/v1/2021.inlg-1.19
%U https://aclanthology.org/2021.inlg-1.19
%U https://doi.org/10.18653/v1/2021.inlg-1.19
%P 184-200
Markdown (Informal)
[Goal-Oriented Script Construction](https://aclanthology.org/2021.inlg-1.19) (Lyu et al., INLG 2021)
ACL
- Qing Lyu, Li Zhang, and Chris Callison-Burch. 2021. Goal-Oriented Script Construction. In Proceedings of the 14th International Conference on Natural Language Generation, pages 184–200, Aberdeen, Scotland, UK. Association for Computational Linguistics.