@inproceedings{luo-etal-2024-open,
title = "Open Event Causality Extraction by the Assistance of {LLM} in Task Annotation, Dataset, and Method",
author = "Luo, Kun and
Zhou, Tong and
Chen, Yubo and
Zhao, Jun and
Liu, Kang",
editor = "Dong, Tiansi and
Hinrichs, Erhard and
Han, Zhen and
Liu, Kang and
Song, Yangqiu and
Cao, Yixin and
Hempelmann, Christian F. and
Sifa, Rafet",
booktitle = "Proceedings of the Workshop: Bridging Neurons and Symbols for Natural Language Processing and Knowledge Graphs Reasoning (NeusymBridge) @ LREC-COLING-2024",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.neusymbridge-1.4",
pages = "33--44",
abstract = "Event Causality Extraction (ECE) aims to extract explicit causal relations between event pairs from the text. However, the event boundary deviation and the causal event pair mismatching are two crucial challenges that remain unaddressed. To address the above issues, we propose a paradigm to utilize LLM to optimize the task definition, evolve the datasets, and strengthen our proposed customized Contextual Highlighting Event Causality Extraction framework (CHECE). Specifically in CHECE, we propose an Event Highlighter and an Event Concretization Module, guiding the model to represent the event by a higher-level cluster and consider its causal counterpart in event boundary prediction to deal with event boundary deviation. And we propose a Contextual Event Causality Matching mechanism, meanwhile, applying LLM to diversify the content templates to force the model to learn causality from context to targeting on causal event pair mismatching. Experimental results on two ECE datasets demonstrate the effectiveness of our method.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="luo-etal-2024-open">
<titleInfo>
<title>Open Event Causality Extraction by the Assistance of LLM in Task Annotation, Dataset, and Method</title>
</titleInfo>
<name type="personal">
<namePart type="given">Kun</namePart>
<namePart type="family">Luo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tong</namePart>
<namePart type="family">Zhou</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yubo</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jun</namePart>
<namePart type="family">Zhao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kang</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the Workshop: Bridging Neurons and Symbols for Natural Language Processing and Knowledge Graphs Reasoning (NeusymBridge) @ LREC-COLING-2024</title>
</titleInfo>
<name type="personal">
<namePart type="given">Tiansi</namePart>
<namePart type="family">Dong</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Erhard</namePart>
<namePart type="family">Hinrichs</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zhen</namePart>
<namePart type="family">Han</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kang</namePart>
<namePart type="family">Liu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yangqiu</namePart>
<namePart type="family">Song</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yixin</namePart>
<namePart type="family">Cao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Christian</namePart>
<namePart type="given">F</namePart>
<namePart type="family">Hempelmann</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Rafet</namePart>
<namePart type="family">Sifa</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>ELRA and ICCL</publisher>
<place>
<placeTerm type="text">Torino, Italia</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Event Causality Extraction (ECE) aims to extract explicit causal relations between event pairs from the text. However, the event boundary deviation and the causal event pair mismatching are two crucial challenges that remain unaddressed. To address the above issues, we propose a paradigm to utilize LLM to optimize the task definition, evolve the datasets, and strengthen our proposed customized Contextual Highlighting Event Causality Extraction framework (CHECE). Specifically in CHECE, we propose an Event Highlighter and an Event Concretization Module, guiding the model to represent the event by a higher-level cluster and consider its causal counterpart in event boundary prediction to deal with event boundary deviation. And we propose a Contextual Event Causality Matching mechanism, meanwhile, applying LLM to diversify the content templates to force the model to learn causality from context to targeting on causal event pair mismatching. Experimental results on two ECE datasets demonstrate the effectiveness of our method.</abstract>
<identifier type="citekey">luo-etal-2024-open</identifier>
<location>
<url>https://aclanthology.org/2024.neusymbridge-1.4</url>
</location>
<part>
<date>2024-05</date>
<extent unit="page">
<start>33</start>
<end>44</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Open Event Causality Extraction by the Assistance of LLM in Task Annotation, Dataset, and Method
%A Luo, Kun
%A Zhou, Tong
%A Chen, Yubo
%A Zhao, Jun
%A Liu, Kang
%Y Dong, Tiansi
%Y Hinrichs, Erhard
%Y Han, Zhen
%Y Liu, Kang
%Y Song, Yangqiu
%Y Cao, Yixin
%Y Hempelmann, Christian F.
%Y Sifa, Rafet
%S Proceedings of the Workshop: Bridging Neurons and Symbols for Natural Language Processing and Knowledge Graphs Reasoning (NeusymBridge) @ LREC-COLING-2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F luo-etal-2024-open
%X Event Causality Extraction (ECE) aims to extract explicit causal relations between event pairs from the text. However, the event boundary deviation and the causal event pair mismatching are two crucial challenges that remain unaddressed. To address the above issues, we propose a paradigm to utilize LLM to optimize the task definition, evolve the datasets, and strengthen our proposed customized Contextual Highlighting Event Causality Extraction framework (CHECE). Specifically in CHECE, we propose an Event Highlighter and an Event Concretization Module, guiding the model to represent the event by a higher-level cluster and consider its causal counterpart in event boundary prediction to deal with event boundary deviation. And we propose a Contextual Event Causality Matching mechanism, meanwhile, applying LLM to diversify the content templates to force the model to learn causality from context to targeting on causal event pair mismatching. Experimental results on two ECE datasets demonstrate the effectiveness of our method.
%U https://aclanthology.org/2024.neusymbridge-1.4
%P 33-44
Markdown (Informal)
[Open Event Causality Extraction by the Assistance of LLM in Task Annotation, Dataset, and Method](https://aclanthology.org/2024.neusymbridge-1.4) (Luo et al., NeusymBridge-WS 2024)
ACL