论文标题
句子披肩数据集用于中国机器阅读理解
A Sentence Cloze Dataset for Chinese Machine Reading Comprehension
论文作者
论文摘要
由于中国NLP社区的持续努力，越来越多的中国机器阅读理解数据集可用。为了在本文中增加该领域的多样性，我们提出了一项名为“句子”式机器阅读理解理解（SC-MRC）的新任务。拟议的任务旨在将正确的候选句子填充到有几个空白的段落中。我们构建了一个名为CMRC 2019的中国数据集，以评估SC-MRC任务的难度。此外，为了增加更多的困难，我们还制作了与正确的候选人相似的假候选人，这需要机器在上下文中判断其正确性。拟议的数据集在10k段落中包含超过100K的空白（问题），这起源于中国叙事故事。为了评估数据集，我们基于预训练的模型实施了多个基线系统，结果表明，最先进的模型仍然以很大的利润使人类的绩效表现不佳。我们发布数据集和基线系统，以进一步促进我们的社区。通过https://github.com/ymcui/cmrc2019获得资源
Owing to the continuous efforts by the Chinese NLP community, more and more Chinese machine reading comprehension datasets become available. To add diversity in this area, in this paper, we propose a new task called Sentence Cloze-style Machine Reading Comprehension (SC-MRC). The proposed task aims to fill the right candidate sentence into the passage that has several blanks. We built a Chinese dataset called CMRC 2019 to evaluate the difficulty of the SC-MRC task. Moreover, to add more difficulties, we also made fake candidates that are similar to the correct ones, which requires the machine to judge their correctness in the context. The proposed dataset contains over 100K blanks (questions) within over 10K passages, which was originated from Chinese narrative stories. To evaluate the dataset, we implement several baseline systems based on the pre-trained models, and the results show that the state-of-the-art model still underperforms human performance by a large margin. We release the dataset and baseline system to further facilitate our community. Resources available through https://github.com/ymcui/cmrc2019
