arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2005.14709cs.CL
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超越排行榜:揭示自然语言推理数据和模型弱点的方法综述

Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models

University of Manchester(曼彻斯特大学)

Viktor Schlegel, Goran Nenadic, Riza Batista-Navarro

摘要:

近年来,越来越多的出版物分析了自然语言推理(NLI)数据集的表面线索,它们是否破坏了这些数据集背后的任务的复杂性,以及它们如何影响这些数据优化和评估的模型。这种结构化的调查提供了一个不断发展的研究领域的概述,通过分类报告的模型和数据集的弱点,并提出了方法,以揭示和减轻这些弱点的英语。我们总结和讨论的结果,并得出结论,为未来可能的研究方向的一组建议。我们希望这将是一个有用的资源,为研究人员谁提出新的数据集,有一套工具来评估他们的数据的适用性和质量,以评估各种感兴趣的现象,以及那些谁开发新的架构,以进一步了解他们的改进的影响,就其模型的后天能力。

英文摘要:

Recent years have seen a growing number of publications that analyse Natural Language Inference (NLI) datasets for superficial cues, whether they undermine the complexity of the tasks underlying those datasets and how they impact those models that are optimised and evaluated on this data. This structured survey provides an overview of the evolving research area by categorising reported weaknesses in models and datasets and the methods proposed to reveal and alleviate those weaknesses for the English language. We summarise and discuss the findings and conclude with a set of recommendations for possible future research directions. We hope it will be a useful resource for researchers who propose new datasets, to have a set of tools to assess the suitability and quality of their data to evaluate various phenomena of interest, as well as those who develop novel architectures, to further understand the implications of their improvements with respect to their model's acquired capabilities.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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