Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets

6 Aug 2020Patrick LewisPontus StenetorpSebastian Riedel

Ideally Open-Domain Question Answering models should exhibit a number of competencies, ranging from simply memorizing questions seen at training time, to answering novel question formulations with answers seen during training, to generalizing to completely novel questions with novel answers. However, single aggregated test set scores do not show the full picture of what capabilities models truly have... (read more)

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