期刊名称:Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度:2021
卷号:2021
页码:1185-1196
DOI:10.18653/v1/2021.eacl-main.101
语种:English
出版社:ACL Anthology
摘要:We propose to leverage lexical paraphrases and high precision rules informed by news discourse structure to automatically collect coreferential and non-coreferential event pairs from unlabeled English news articles. We perform both manual validation and empirical evaluation on multiple evaluation datasets with different event domains and text genres to assess the quality of our acquired event pairs. We found that a model trained on our acquired event pairs performs comparably as the supervised model when applied to new data out of the training data domains. Further, augmenting human-annotated data with the acquired event pairs provides empirical performance gains on both in-domain and out-of-domain evaluation datasets.