Zero-shot Event Extraction via Transfer Learning: Challenges and Insights
Event extraction has long been a challenging task, addressed mostly with supervised methods that require expensive annotation and are not extensible to new event ontologies. In this work, we explore the possibility of zero-shot event extraction by formulating it as a set of Textual Entailment (TE) and/or Question Answering (QA) queries (e.g. {`}A city was attacked{''} entails {`}There is an attack{''}), exploiting pretrained TE/QA models for direct transfer. On ACE-2005 and ERE, our system achieves acceptable results, yet there is still a large gap from supervised approaches, showing that current QA and TE technologies fail in transferring to a different domain. To investigate the reasons behind the gap, we analyze the remaining key challenges, their respective impact, and possible improvement directions.
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Event ExtractionNatural Language InferenceQuestion AnsweringTransfer LearningZero-shot Event ExtractionSimilar Papers 제목 키워드 기반
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