ReCoRD

ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension. We present a large-scale dataset, ReCoRD, for machine reading comprehension requiring commonsense reasoning. Experiments on this dataset demonstrate that the performance of state-of-the-art MRC systems fall far behind human performance. ReCoRD represents a challenge for future research to bridge the gap between human and machine commonsense reading comprehension. ReCoRD is available at https://sheng-z.github.io/ReCoRD-explorer/ ( arXiv:1810.12885)

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References in zbMATH (referenced in 3 articles , 1 standard article )

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  1. Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al.: Language Models are Few-Shot Learners (2020) arXiv
  2. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Jamie Brew: HuggingFace’s Transformers: State-of-the-art Natural Language Processing (2019) arXiv
  3. Sheng Zhang, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Kevin Duh, Benjamin Van Durme: ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension (2018) arXiv