Lei Yang


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面向垂直领域的阅读理解数据增强方法(Method for reading comprehension data enhancement in vertical field)
Zhengwei Lv (吕政伟) | Lei Yang (杨雷) | Zhizhong Shi (石智中) | Xiao Liang (梁霄) | Tao Lei (雷涛) | Duoxing Liu (刘多星)
Proceedings of the 19th Chinese National Conference on Computational Linguistics



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AUTOHOME-ORCA at SemEval-2019 Task 8: Application of BERT for Fact-Checking in Community Forums
Zhengwei Lv | Duoxing Liu | Haifeng Sun | Xiao Liang | Tao Lei | Zhizhong Shi | Feng Zhu | Lei Yang
Proceedings of the 13th International Workshop on Semantic Evaluation

Fact checking is an important task for maintaining high quality posts and improving user experience in Community Question Answering forums. Therefore, the SemEval-2019 task 8 is aimed to identify factual question (subtask A) and detect true factual information from corresponding answers (subtask B). In order to address this task, we propose a system based on the BERT model with meta information of questions. For the subtask A, the outputs of fine-tuned BERT classification model are combined with the feature of length of questions to boost the performance. For the subtask B, the predictions of several variants of BERT model encoding the meta information are combined to create an ensemble model. Our system achieved competitive results with an accuracy of 0.82 in the subtask A and 0.83 in the subtask B. The experimental results validate the effectiveness of our system.