To what extent does incorporating unanswerable questions through negative sampling techniques improve performa

Abstract

Abstract: We present the results of the Machine Reading for Question Answering (MRQA) 2019 shared task on evaluating the generalization capabilities of reading comprehension systems. 1 In this task, we adapted and unified 18 distinct question answering datasets into the same format. Among them, six datasets were made available for training, six datasets were made available for development, and the final six were hidden for final evaluation. Ten teams submitted systems, which explored various ideas including data sampling, multi-task learning, adversarial training, and ensembling. The best system achieve

Research Question

To what extent does incorporating unanswerable questions through negative sampling techniques improve performance on the MRQA datasets when evaluated on the SQuAD 2.0 and MRQA out-of-distribution splits?

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Quality Dimensions

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Correction Record

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Provenance