@inproceedings{hu-etal-2025-joint,
title = "Joint Learning Event-Specific Probe and Argument Library with Differential Optimization for Document-Level Multi-Event Extraction",
author = "Hu, Jianpeng and
Xue, Chao and
Yu, Chunqing and
Xu, JiaCheng and
Tan, Chengxiang",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.42/",
doi = "10.18653/v1/2025.findings-naacl.42",
pages = "714--726",
ISBN = "979-8-89176-195-7",
abstract = "Document-level multi-event extraction aims to identify a list of event types and corresponding arguments from the document. However, most of the current methods neglect the fine-grained difference among events in multi-event documents, which leads to event confusion and missing. This is also one of the reasons why the recall and F1-score of multi-event recognition are lower compared to single-event recognition. In this paper, we propose an event-specific probe-based method to sniff multiple events by querying each corresponding argument library, which uses a novel probe-label alignment method for differential optimization. In addition, the role contrastive loss and probe consistent loss are designed to fine-tune the fine-grained role differences and probe differences in each event. The experimental results on two general datasets show that our method outperforms the state-of-the-art method in the F1-score, especially in the recall of multi-events."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="hu-etal-2025-joint">
<titleInfo>
<title>Joint Learning Event-Specific Probe and Argument Library with Differential Optimization for Document-Level Multi-Event Extraction</title>
</titleInfo>
<name type="personal">
<namePart type="given">Jianpeng</namePart>
<namePart type="family">Hu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chao</namePart>
<namePart type="family">Xue</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chunqing</namePart>
<namePart type="family">Yu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">JiaCheng</namePart>
<namePart type="family">Xu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Chengxiang</namePart>
<namePart type="family">Tan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-04</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: NAACL 2025</title>
</titleInfo>
<name type="personal">
<namePart type="given">Luis</namePart>
<namePart type="family">Chiruzzo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alan</namePart>
<namePart type="family">Ritter</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Lu</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Albuquerque, New Mexico</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-195-7</identifier>
</relatedItem>
<abstract>Document-level multi-event extraction aims to identify a list of event types and corresponding arguments from the document. However, most of the current methods neglect the fine-grained difference among events in multi-event documents, which leads to event confusion and missing. This is also one of the reasons why the recall and F1-score of multi-event recognition are lower compared to single-event recognition. In this paper, we propose an event-specific probe-based method to sniff multiple events by querying each corresponding argument library, which uses a novel probe-label alignment method for differential optimization. In addition, the role contrastive loss and probe consistent loss are designed to fine-tune the fine-grained role differences and probe differences in each event. The experimental results on two general datasets show that our method outperforms the state-of-the-art method in the F1-score, especially in the recall of multi-events.</abstract>
<identifier type="citekey">hu-etal-2025-joint</identifier>
<identifier type="doi">10.18653/v1/2025.findings-naacl.42</identifier>
<location>
<url>https://aclanthology.org/2025.findings-naacl.42/</url>
</location>
<part>
<date>2025-04</date>
<extent unit="page">
<start>714</start>
<end>726</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Joint Learning Event-Specific Probe and Argument Library with Differential Optimization for Document-Level Multi-Event Extraction
%A Hu, Jianpeng
%A Xue, Chao
%A Yu, Chunqing
%A Xu, JiaCheng
%A Tan, Chengxiang
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F hu-etal-2025-joint
%X Document-level multi-event extraction aims to identify a list of event types and corresponding arguments from the document. However, most of the current methods neglect the fine-grained difference among events in multi-event documents, which leads to event confusion and missing. This is also one of the reasons why the recall and F1-score of multi-event recognition are lower compared to single-event recognition. In this paper, we propose an event-specific probe-based method to sniff multiple events by querying each corresponding argument library, which uses a novel probe-label alignment method for differential optimization. In addition, the role contrastive loss and probe consistent loss are designed to fine-tune the fine-grained role differences and probe differences in each event. The experimental results on two general datasets show that our method outperforms the state-of-the-art method in the F1-score, especially in the recall of multi-events.
%R 10.18653/v1/2025.findings-naacl.42
%U https://aclanthology.org/2025.findings-naacl.42/
%U https://doi.org/10.18653/v1/2025.findings-naacl.42
%P 714-726
Markdown (Informal)
[Joint Learning Event-Specific Probe and Argument Library with Differential Optimization for Document-Level Multi-Event Extraction](https://aclanthology.org/2025.findings-naacl.42/) (Hu et al., Findings 2025)
ACL