ReCO: A Large Scale Chinese Reading Comprehension Dataset on Opinion


  • Bingning Wang Sogou
  • Ting Yao Sogou
  • Qi Zhang Sogou
  • Jingfang Xu Sogou
  • Xiaochuan Wang Sogou



This paper presents the ReCO, a human-curated Chinese Reading Comprehension dataset on Opinion. The questions in ReCO are opinion based queries issued to commercial search engine. The passages are provided by the crowdworkers who extract the support snippet from the retrieved documents. Finally, an abstractive yes/no/uncertain answer was given by the crowdworkers. The release of ReCO consists of 300k questions that to our knowledge is the largest in Chinese reading comprehension. A prominent characteristic of ReCO is that in addition to the original context paragraph, we also provided the support evidence that could be directly used to answer the question. Quality analysis demonstrates the challenge of ReCO that it requires various types of reasoning skills such as causal inference, logical reasoning, etc. Current QA models that perform very well on many question answering problems, such as BERT (Devlin et al. 2018), only achieves 77% accuracy on this dataset, a large margin behind humans nearly 92% performance, indicating ReCO present a good challenge for machine reading comprehension. The codes, dataset and leaderboard will be freely available at




How to Cite

Wang, B., Yao, T., Zhang, Q., Xu, J., & Wang, X. (2020). ReCO: A Large Scale Chinese Reading Comprehension Dataset on Opinion. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 9146-9153.



AAAI Technical Track: Natural Language Processing