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    <abstract>We present RACE, a new dataset for benchmark evaluation of methods in the reading comprehension task. Collected from the English exams for middle and high school Chinese students in the age range between 12 to 18, RACE consists of near 28,000 passages and near 100,000 questions generated by human experts (English instructors), and covers a variety of topics which are carefully designed for evaluating the students’ ability in understanding and reasoning. In particular, the proportion of questions that requires reasoning is much larger in RACE than that in other benchmark datasets for reading comprehension, and there is a significant gap between the performance of the state-of-the-art models (43%) and the ceiling human performance (95%). We hope this new dataset can serve as a valuable resource for research and evaluation in machine comprehension. The dataset is freely available at http://www.cs.cmu.edu/ glai1/data/race/and the code is available at https://github.com/qizhex/RACE_AR_baselines.</abstract>
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%0 Conference Proceedings
%T RACE: Large-scale ReAding Comprehension Dataset From Examinations
%A Lai, Guokun
%A Xie, Qizhe
%A Liu, Hanxiao
%A Yang, Yiming
%A Hovy, Eduard
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F lai-etal-2017-race
%X We present RACE, a new dataset for benchmark evaluation of methods in the reading comprehension task. Collected from the English exams for middle and high school Chinese students in the age range between 12 to 18, RACE consists of near 28,000 passages and near 100,000 questions generated by human experts (English instructors), and covers a variety of topics which are carefully designed for evaluating the students’ ability in understanding and reasoning. In particular, the proportion of questions that requires reasoning is much larger in RACE than that in other benchmark datasets for reading comprehension, and there is a significant gap between the performance of the state-of-the-art models (43%) and the ceiling human performance (95%). We hope this new dataset can serve as a valuable resource for research and evaluation in machine comprehension. The dataset is freely available at http://www.cs.cmu.edu/ glai1/data/race/and the code is available at https://github.com/qizhex/RACE_AR_baselines.
%R 10.18653/v1/D17-1082
%U https://aclanthology.org/D17-1082
%U https://doi.org/10.18653/v1/D17-1082
%P 785-794

Markdown (Informal)

[RACE: Large-scale ReAding Comprehension Dataset From Examinations](https://aclanthology.org/D17-1082) (Lai et al., EMNLP 2017)

• RACE: Large-scale ReAding Comprehension Dataset From Examinations (Lai et al., EMNLP 2017)

ACL

• Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy. 2017. RACE: Large-scale ReAding Comprehension Dataset From Examinations. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 785–794, Copenhagen, Denmark. Association for Computational Linguistics.