QAEVENT: Event Extraction as Question-Answer Pairs Generation

Anonymous

Abstract: We propose a novel representation of document-level events as question and answer pairs (QAEVENT). Under this paradigm: (1) questions themselves can define argument roles without the need for predefined schemas, which will cover a comprehensive list of event arguments from the document; (2) it allows for more scalable and faster annotations from crowdworkers without linguistic expertise. Based on our new paradigm, we collect a novel and wide-coverage dataset. Our examinations show that annotations with the QA representations produce high-quality data for document-level event extraction, both in terms of human agreement level and high coverage of roles comparing to the pre-defined schema. We present and compare representative approaches for generating event question answer pairs on our benchmark.

Paper Type: long

Research Area: Information Extraction

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Data resources

Languages Studied: English

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