This repository contains the data for The Second Evaluation Workshop on Chinese Machine Reading Comprehension (CMRC 2018). We will present our paper on EMNLP 2019.
Title: A Span-Extraction Dataset for Chinese Machine Reading Comprehension
Authors: Yiming Cui, Ting Liu, Wanxiang Che, Li Xiao, Zhipeng Chen, Wentao Ma, Shijin Wang, Guoping Hu
Link: https://www.aclweb.org/anthology/D19-1600/
Venue: EMNLP-IJCNLP 2019
Keep track of the latest state-of-the-art systems on CMRC 2018 dataset.
https://ymcui.github.io/cmrc2018/
Please download CMRC 2018 public datasets via the following CodaLab Worksheet.
https://worksheets.codalab.org/worksheets/0x92a80d2fab4b4f79a2b4064f7ddca9ce
If you would like to test your model on the hidden test and challenge set, please follow the instructions on how to submit your model via CodaLab worksheet.
https://worksheets.codalab.org/worksheets/0x96f61ee5e9914aee8b54bd11e66ec647/
Note that the test set on CLUE is NOT the complete test set. If you wish to evaluate your model OFFICIALLY on CMRC 2018, you should follow the guidelines here.
You can also access this dataset as part of the HuggingFace datasets
library library as follow:
!pip install datasets
from datasets import load_dataset
dataset = load_dataset('cmrc2018')
More details on the options and usage for this library can be found on the nlp
repository at https://github.com/huggingface/nlp
If you wish to use our data in your research, please cite:
@inproceedings{cui-emnlp2019-cmrc2018,
title = "A Span-Extraction Dataset for {C}hinese Machine Reading Comprehension",
author = "Cui, Yiming and
Liu, Ting and
Che, Wanxiang and
Xiao, Li and
Chen, Zhipeng and
Ma, Wentao and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D19-1600",
doi = "10.18653/v1/D19-1600",
pages = "5886--5891",
}
ISLRN: 013-662-947-043-2
http://www.islrn.org/resources/resources_info/7952/
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