Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension

Three New RC Datasets We have created three new Reading Comprehension datasets constructed using an adversarial model-in-the-loop. We use three different models; BiDAF (Seo et al., 2016), BERTLarge (Devlin et al., 2018), and RoBERTaLarge (Liu et al., 2019) in the annotation loop and construct three datasets; D(BiDAF), D(BERT), and D(RoBERTa), each with 10,000 training examples, 1,000 validation, and 1,000 test examples. The adversarial human annotation paradigm ensures that these datasets

1 mentions: @max_nlp
Keywords: annotation
Date: 2021/01/13 17:21

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@max_nlp How well do your RC models perform on more challenging questions? adversarialQA (t.co/RExQ6UALOV) is now available for easy access in @huggingface datasets! t.co/hmhdj6OjkB

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