Automatic Data Acquisition for Event Coreference Resolution
Automatic Data Acquisition for Event Coreference Resolution
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DOI：
10.18653/v1/2021.eacl-main.101
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Prafulla Kumar Choubey;Ruihong Huang
中科院分区：
文献类型：
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作者：
Prafulla Kumar Choubey;Ruihong Huang
我们建议利用词汇释义和高精度的规则通知的新闻语篇结构，自动收集共指和非共指事件对从未标记的英语新闻文章。我们对具有不同事件域和文本类型的多个评估数据集进行手动验证和经验评估，以评估我们获得的事件对的质量。我们发现，在我们获得的事件对上训练的模型在应用于训练数据域之外的新数据时，表现得像监督模型一样。此外，用所获取的事件对增强人类注释的数据在域内和域外评估数据集上提供了经验性能增益。
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.