@inproceedings{wei-etal-2022-desed,
title = "{DESED}: Dialogue-based Explanation for Sentence-level Event Detection",
author = "Wei, Yinyi and
Liu, Shuaipeng and
Lv, Jianwei and
Xi, Xiangyu and
Yan, Hailei and
Ye, Wei and
Mo, Tong and
Yang, Fan and
Wan, Guanglu",
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-1.219",
pages = "2483--2493",
abstract = "Many recent sentence-level event detection efforts focus on enriching sentence semantics, e.g., via multi-task or prompt-based learning. Despite the promising performance, these methods commonly depend on label-extensive manual annotations or require domain expertise to design sophisticated templates and rules. This paper proposes a new paradigm, named dialogue-based explanation, to enhance sentence semantics for event detection. By saying dialogue-based explanation of an event, we mean explaining it through a consistent information-intensive dialogue, with the original event description as the start utterance. We propose three simple dialogue generation methods, whose outputs are then fed into a hybrid attention mechanism to characterize the complementary event semantics. Extensive experimental results on two event detection datasets verify the effectiveness of our method and suggest promising research opportunities in the dialogue-based explanation paradigm.",
}
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<abstract>Many recent sentence-level event detection efforts focus on enriching sentence semantics, e.g., via multi-task or prompt-based learning. Despite the promising performance, these methods commonly depend on label-extensive manual annotations or require domain expertise to design sophisticated templates and rules. This paper proposes a new paradigm, named dialogue-based explanation, to enhance sentence semantics for event detection. By saying dialogue-based explanation of an event, we mean explaining it through a consistent information-intensive dialogue, with the original event description as the start utterance. We propose three simple dialogue generation methods, whose outputs are then fed into a hybrid attention mechanism to characterize the complementary event semantics. Extensive experimental results on two event detection datasets verify the effectiveness of our method and suggest promising research opportunities in the dialogue-based explanation paradigm.</abstract>
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%0 Conference Proceedings
%T DESED: Dialogue-based Explanation for Sentence-level Event Detection
%A Wei, Yinyi
%A Liu, Shuaipeng
%A Lv, Jianwei
%A Xi, Xiangyu
%A Yan, Hailei
%A Ye, Wei
%A Mo, Tong
%A Yang, Fan
%A Wan, Guanglu
%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 wei-etal-2022-desed
%X Many recent sentence-level event detection efforts focus on enriching sentence semantics, e.g., via multi-task or prompt-based learning. Despite the promising performance, these methods commonly depend on label-extensive manual annotations or require domain expertise to design sophisticated templates and rules. This paper proposes a new paradigm, named dialogue-based explanation, to enhance sentence semantics for event detection. By saying dialogue-based explanation of an event, we mean explaining it through a consistent information-intensive dialogue, with the original event description as the start utterance. We propose three simple dialogue generation methods, whose outputs are then fed into a hybrid attention mechanism to characterize the complementary event semantics. Extensive experimental results on two event detection datasets verify the effectiveness of our method and suggest promising research opportunities in the dialogue-based explanation paradigm.
%U https://aclanthology.org/2022.coling-1.219
%P 2483-2493
Markdown (Informal)
[DESED: Dialogue-based Explanation for Sentence-level Event Detection](https://aclanthology.org/2022.coling-1.219) (Wei et al., COLING 2022)
ACL
- Yinyi Wei, Shuaipeng Liu, Jianwei Lv, Xiangyu Xi, Hailei Yan, Wei Ye, Tong Mo, Fan Yang, and Guanglu Wan. 2022. DESED: Dialogue-based Explanation for Sentence-level Event Detection. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2483–2493, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.