@inproceedings{sun-etal-2023-incorporating,
title = "Incorporating Task-Specific Concept Knowledge into Script Learning",
author = "Sun, Chenkai and
Xu, Tie and
Zhai, ChengXiang and
Ji, Heng",
editor = "Vlachos, Andreas and
Augenstein, Isabelle",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.eacl-main.220",
doi = "10.18653/v1/2023.eacl-main.220",
pages = "3026--3040",
abstract = "In this paper, we present Tetris, a new task of Goal-Oriented Script Completion. Unlike previous work, it considers a more realistic and general setting, where the input includes not only the goal but also additional user context, including preferences and history. To address this problem, we propose a novel approach, which uses two techniques to improve performance: (1) concept prompting, and (2) script-oriented contrastive learning that addresses step repetition and hallucination problems. On our WikiHow-based dataset, we find that both methods improve performance.",
}
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%0 Conference Proceedings
%T Incorporating Task-Specific Concept Knowledge into Script Learning
%A Sun, Chenkai
%A Xu, Tie
%A Zhai, ChengXiang
%A Ji, Heng
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F sun-etal-2023-incorporating
%X In this paper, we present Tetris, a new task of Goal-Oriented Script Completion. Unlike previous work, it considers a more realistic and general setting, where the input includes not only the goal but also additional user context, including preferences and history. To address this problem, we propose a novel approach, which uses two techniques to improve performance: (1) concept prompting, and (2) script-oriented contrastive learning that addresses step repetition and hallucination problems. On our WikiHow-based dataset, we find that both methods improve performance.
%R 10.18653/v1/2023.eacl-main.220
%U https://aclanthology.org/2023.eacl-main.220
%U https://doi.org/10.18653/v1/2023.eacl-main.220
%P 3026-3040
Markdown (Informal)
[Incorporating Task-Specific Concept Knowledge into Script Learning](https://aclanthology.org/2023.eacl-main.220) (Sun et al., EACL 2023)
ACL