@inproceedings{li-etal-2024-advancing-event,
title = "Advancing Event Causality Identification via Heuristic Semantic Dependency Inquiry Network",
author = "Li, Haoran and
Gao, Qiang and
Wu, Hongmei and
Huang, Li",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.87/",
doi = "10.18653/v1/2024.emnlp-main.87",
pages = "1467--1478",
abstract = "Event Causality Identification (ECI) focuses on extracting causal relations between events in texts. Existing methods for ECI primarily rely on causal features and external knowledge. However, these approaches fall short in two dimensions: (1) causal features between events in a text often lack explicit clues, and (2) external knowledge may introduce bias, while specific problems require tailored analyses. To address these issues, we propose SemDI - a simple and effective \textbf{Sem}antic \textbf{D}ependency \textbf{I}nquiry Network for ECI. SemDI captures semantic dependencies within the context using a unified encoder. Then, it utilizes a \textit{Cloze} Analyzer to generate a fill-in token based on comprehensive context understanding. Finally, this fill-in token is used to inquire about the causal relation between two events. Extensive experiments demonstrate the effectiveness of SemDI, surpassing state-of-the-art methods on three widely used benchmarks. Code is available at \url{https://github.com/hrlics/SemDI}."
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<abstract>Event Causality Identification (ECI) focuses on extracting causal relations between events in texts. Existing methods for ECI primarily rely on causal features and external knowledge. However, these approaches fall short in two dimensions: (1) causal features between events in a text often lack explicit clues, and (2) external knowledge may introduce bias, while specific problems require tailored analyses. To address these issues, we propose SemDI - a simple and effective Semantic Dependency Inquiry Network for ECI. SemDI captures semantic dependencies within the context using a unified encoder. Then, it utilizes a Cloze Analyzer to generate a fill-in token based on comprehensive context understanding. Finally, this fill-in token is used to inquire about the causal relation between two events. Extensive experiments demonstrate the effectiveness of SemDI, surpassing state-of-the-art methods on three widely used benchmarks. Code is available at https://github.com/hrlics/SemDI.</abstract>
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%0 Conference Proceedings
%T Advancing Event Causality Identification via Heuristic Semantic Dependency Inquiry Network
%A Li, Haoran
%A Gao, Qiang
%A Wu, Hongmei
%A Huang, Li
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F li-etal-2024-advancing-event
%X Event Causality Identification (ECI) focuses on extracting causal relations between events in texts. Existing methods for ECI primarily rely on causal features and external knowledge. However, these approaches fall short in two dimensions: (1) causal features between events in a text often lack explicit clues, and (2) external knowledge may introduce bias, while specific problems require tailored analyses. To address these issues, we propose SemDI - a simple and effective Semantic Dependency Inquiry Network for ECI. SemDI captures semantic dependencies within the context using a unified encoder. Then, it utilizes a Cloze Analyzer to generate a fill-in token based on comprehensive context understanding. Finally, this fill-in token is used to inquire about the causal relation between two events. Extensive experiments demonstrate the effectiveness of SemDI, surpassing state-of-the-art methods on three widely used benchmarks. Code is available at https://github.com/hrlics/SemDI.
%R 10.18653/v1/2024.emnlp-main.87
%U https://aclanthology.org/2024.emnlp-main.87/
%U https://doi.org/10.18653/v1/2024.emnlp-main.87
%P 1467-1478
Markdown (Informal)
[Advancing Event Causality Identification via Heuristic Semantic Dependency Inquiry Network](https://aclanthology.org/2024.emnlp-main.87/) (Li et al., EMNLP 2024)
ACL