@inproceedings{zhang-etal-2026-enhancing-event,
title = "Enhancing Event Candidate Acquisition for Event Linking",
author = "Zhang, Ziyang and
Liu, Yinan and
Xue, Boyi and
Huang, Yingxuan and
Wang, Bin and
Yang, Xiaochun",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.922/",
pages = "19459--19469",
ISBN = "979-8-89176-462-0",
abstract = "Event linking associates event mentions in text with entries in a knowledge base (KB), or identifies them as out-of-KB events. Although existing methods use different architectures, candidate event acquisition can still be weakened by short ambiguous mentions, noisy arguments, and evidence that is unevenly useful for retrieval. We present MACE, a Multi-Agent Candidate Event acquisition method that refines event structure before linking. MACE uses evidence-specialized LLM agents to acquire time, location, participant, and event-type evidence, exposes intermediate queries to candidate-event lookup tools, and lets a coordinator revise the evidence set before final candidate construction. Experiments on two event linking benchmarks show that adding MACE to different event linking models consistently improves accuracy. These results show that MACE improves event linking through better candidate event acquisition without modifying the event linking model."
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<abstract>Event linking associates event mentions in text with entries in a knowledge base (KB), or identifies them as out-of-KB events. Although existing methods use different architectures, candidate event acquisition can still be weakened by short ambiguous mentions, noisy arguments, and evidence that is unevenly useful for retrieval. We present MACE, a Multi-Agent Candidate Event acquisition method that refines event structure before linking. MACE uses evidence-specialized LLM agents to acquire time, location, participant, and event-type evidence, exposes intermediate queries to candidate-event lookup tools, and lets a coordinator revise the evidence set before final candidate construction. Experiments on two event linking benchmarks show that adding MACE to different event linking models consistently improves accuracy. These results show that MACE improves event linking through better candidate event acquisition without modifying the event linking model.</abstract>
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%0 Conference Proceedings
%T Enhancing Event Candidate Acquisition for Event Linking
%A Zhang, Ziyang
%A Liu, Yinan
%A Xue, Boyi
%A Huang, Yingxuan
%A Wang, Bin
%A Yang, Xiaochun
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F zhang-etal-2026-enhancing-event
%X Event linking associates event mentions in text with entries in a knowledge base (KB), or identifies them as out-of-KB events. Although existing methods use different architectures, candidate event acquisition can still be weakened by short ambiguous mentions, noisy arguments, and evidence that is unevenly useful for retrieval. We present MACE, a Multi-Agent Candidate Event acquisition method that refines event structure before linking. MACE uses evidence-specialized LLM agents to acquire time, location, participant, and event-type evidence, exposes intermediate queries to candidate-event lookup tools, and lets a coordinator revise the evidence set before final candidate construction. Experiments on two event linking benchmarks show that adding MACE to different event linking models consistently improves accuracy. These results show that MACE improves event linking through better candidate event acquisition without modifying the event linking model.
%U https://aclanthology.org/2026.findings-emnlp.922/
%P 19459-19469
Markdown (Informal)
[Enhancing Event Candidate Acquisition for Event Linking](https://aclanthology.org/2026.findings-emnlp.922/) (Zhang et al., Findings 2026)
ACL
- Ziyang Zhang, Yinan Liu, Boyi Xue, Yingxuan Huang, Bin Wang, and Xiaochun Yang. 2026. Enhancing Event Candidate Acquisition for Event Linking. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 19459–19469, Budapest, Hungary. Association for Computational Linguistics.