64 resources

  • Zichao Wang, Jakob Valdez, Debshila Basu...
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    Dec 1st, 2022
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    bookSection
    Zichao Wang, Jakob Valdez, Debshila Basu...
    Dec 1st, 2022
  • Ying Xu, Dakuo Wang, Mo Yu
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    Dec 1st, 2022
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    journalArticle
    Ying Xu, Dakuo Wang, Mo Yu
    Dec 1st, 2022

    Question answering (QA) is a fundamental means to facilitate assessment and training of narrative comprehension skills for both machines and young children, yet there is scarcity of high-quality QA datasets carefully designed to serve this purpose. In particular, existing datasets rarely distinguish fine-grained reading skills, such as the understanding of varying narrative elements. Drawing on the reading education research, we introduce FairytaleQA, a dataset focusing on narrative...

  • Ming Zhong, Yang Liu, Da Yin
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    Dec 1st, 2022
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    preprint
    Ming Zhong, Yang Liu, Da Yin
    Dec 1st, 2022

    Multi-dimensional evaluation is the dominant paradigm for human evaluation in Natural Language Generation (NLG), i.e., evaluating the generated text from multiple explainable dimensions, such as coherence and fluency. However, automatic evaluation in NLG is still dominated by similarity-based metrics, and we lack a reliable framework for a more comprehensive evaluation of advanced models. In this paper, we propose a unified multi-dimensional evaluator UniEval for NLG. We re-frame NLG...

  • Ming Zhong, Yang Liu, Da Yin
    |
    Dec 1st, 2022
    |
    preprint
    Ming Zhong, Yang Liu, Da Yin
    Dec 1st, 2022

    Multi-dimensional evaluation is the dominant paradigm for human evaluation in Natural Language Generation (NLG), i.e., evaluating the generated text from multiple explainable dimensions, such as coherence and fluency. However, automatic evaluation in NLG is still dominated by similarity-based metrics, and we lack a reliable framework for a more comprehensive evaluation of advanced models. In this paper, we propose a unified multi-dimensional evaluator UniEval for NLG. We re-frame NLG...

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