A Sentence Cloze Dataset for Chinese Machine Reading Comprehension

By Yiming Cui, Ting-Wei Liu, Ziqing Yang, Zhipeng Chen, Wentao Ma, Wanxiang Che, Shijin Wang, Guoping Hu. 2020

20 citations recorded by the source corpus.

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

Subfields: Artificial Intelligence, Natural Language Processing, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Xin Jiang
  2. Qun Liu
  3. Shanzhi Gu
  4. Yue Yu
  5. Yi Liao
  6. Wei Zeng
  7. Jun Wang
  8. Yonghong Tian

In Natural Language Processing

  1. Qun Liu
  2. Yi Liao
  3. Xin Jiang
  4. Jun Wang
  5. Yonghong Tian
  6. Chen Li
  7. Yaowei Wang
  8. Andy Way

In Evaluation & Benchmarks

  1. Qun Liu
  2. Yi Liao
  3. Xin Jiang
  4. Yonghong Tian
  5. Chen Li
  6. Yaowei Wang

Citations in the corpus