Modeling Document-level Causal Structures for Event Causal Relation Identification

Lei Gao, Prafulla Kumar Choubey, Ruihong Huang


Abstract
We aim to comprehensively identify all the event causal relations in a document, both within a sentence and across sentences, which is important for reconstructing pivotal event structures. The challenges we identified are two: 1) event causal relations are sparse among all possible event pairs in a document, in addition, 2) few causal relations are explicitly stated. Both challenges are especially true for identifying causal relations between events across sentences. To address these challenges, we model rich aspects of document-level causal structures for achieving comprehensive causal relation identification. The causal structures include heavy involvements of document-level main events in causal relations as well as several types of fine-grained constraints that capture implications from certain sentential syntactic relations and discourse relations as well as interactions between event causal relations and event coreference relations. Our experimental results show that modeling the global and fine-grained aspects of causal structures using Integer Linear Programming (ILP) greatly improves the performance of causal relation identification, especially in identifying cross-sentence causal relations.
Anthology ID:
N19-1179
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1808–1817
Language:
URL:
https://aclanthology.org/N19-1179
DOI:
10.18653/v1/N19-1179
Bibkey:
Cite (ACL):
Lei Gao, Prafulla Kumar Choubey, and Ruihong Huang. 2019. Modeling Document-level Causal Structures for Event Causal Relation Identification. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1808–1817, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Modeling Document-level Causal Structures for Event Causal Relation Identification (Gao et al., NAACL 2019)
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PDF:
https://aclanthology.org/N19-1179.pdf