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The dataset contains question-answer pairs to model verbal predicate-argument structure. The questions start with wh-words (Who, What, Where, What, etc.) and contain a verb predicate in the sentence; the answers are phrases in the sentence. This dataset, a.k.a "QASRL-GS" (Gold Standard) or "QASRL-2020", which was constructed via controlled crowdsourcing, includes high-quality QA-SRL annotations to serve as an evaluation set (dev and test) for models trained on the large-scale QA-SRL dataset (you can find it in this hub as biu-nlp/qa_srl2018).
See the paper for details: Controlled Crowdsourcing for High-Quality QA-SRL Annotation, Roit et. al., 2020.
Check out our GitHub repository to find code for evaluation.
The dataset was annotated by selected workers from Amazon Mechanical Turk.