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        <title>Joint Constrained Learning for Event-Event Relation Extraction</title>
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        <namePart type="given">Haoyu</namePart>
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    <name type="personal">
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    <abstract>Understanding natural language involves recognizing how multiple event mentions structurally and temporally interact with each other. In this process, one can induce event complexes that organize multi-granular events with temporal order and membership relations interweaving among them. Due to the lack of jointly labeled data for these relational phenomena and the restriction on the structures they articulate, we propose a joint constrained learning framework for modeling event-event relations. Specifically, the framework enforces logical constraints within and across multiple temporal and subevent relations of events by converting these constraints into differentiable learning objectives. We show that our joint constrained learning approach effectively compensates for the lack of jointly labeled data, and outperforms SOTA methods on benchmarks for both temporal relation extraction and event hierarchy construction, replacing a commonly used but more expensive global inference process. We also present a promising case study to show the effectiveness of our approach to inducing event complexes on an external corpus.</abstract>
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    <identifier type="doi">10.18653/v1/2020.emnlp-main.51</identifier>
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        <url>https://aclanthology.org/2020.emnlp-main.51</url>
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        <date>2020-nov</date>
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            <start>696</start>
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%0 Conference Proceedings
%T Joint Constrained Learning for Event-Event Relation Extraction
%A Wang, Haoyu
%A Chen, Muhao
%A Zhang, Hongming
%A Roth, Dan
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 nov
%I Association for Computational Linguistics
%C Online
%F wang-etal-2020-joint
%X Understanding natural language involves recognizing how multiple event mentions structurally and temporally interact with each other. In this process, one can induce event complexes that organize multi-granular events with temporal order and membership relations interweaving among them. Due to the lack of jointly labeled data for these relational phenomena and the restriction on the structures they articulate, we propose a joint constrained learning framework for modeling event-event relations. Specifically, the framework enforces logical constraints within and across multiple temporal and subevent relations of events by converting these constraints into differentiable learning objectives. We show that our joint constrained learning approach effectively compensates for the lack of jointly labeled data, and outperforms SOTA methods on benchmarks for both temporal relation extraction and event hierarchy construction, replacing a commonly used but more expensive global inference process. We also present a promising case study to show the effectiveness of our approach to inducing event complexes on an external corpus.
%R 10.18653/v1/2020.emnlp-main.51
%U https://aclanthology.org/2020.emnlp-main.51
%U https://doi.org/10.18653/v1/2020.emnlp-main.51
%P 696-706

Markdown (Informal)

[Joint Constrained Learning for Event-Event Relation Extraction](https://aclanthology.org/2020.emnlp-main.51) (Wang et al., EMNLP 2020)

• Joint Constrained Learning for Event-Event Relation Extraction (Wang et al., EMNLP 2020)

ACL

• Haoyu Wang, Muhao Chen, Hongming Zhang, and Dan Roth. 2020. Joint Constrained Learning for Event-Event Relation Extraction. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 696–706, Online. Association for Computational Linguistics.