Conundrums in Event Coreference Resolution: Making Sense of the State of the Art
Jing Lu, Vincent Ng
2021年份
13被引次数
2顶会引用
摘要
Despite recent promising results achieved by span-based approaches to event coreference resolution, there is a lack of understanding of what has been improved. We present an empirical analysis of our state-of-the-art span-based event coreference resolver (Lu and Ng, 2021) with the goal of providing the general NLP audience with a better understanding of the state of the art and coreference researchers with directions for future research.
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- MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation ExtractionXiaozhi Wang, Yulin Chen, Ning Ding, Hao Peng 等EMNLP 2022 · 被引用 35 次
- Improving Large Language Models in Event Relation Logical PredictionMeiqi Chen, Yubo Ma, Kaitao Song, Yixin Cao 等ACL 2024 · 被引用 7 次
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