Abstract：This paper describes the model proposed in “2018 NLP Challenge on Machine Reading Comprehension” by ZWYC team. Treated the machine reading comprehension as extracting the text span from the documents, this paper proposes a feature-rich neural interaction network. In order to effectively use the information of golden answers, our model first reconstructs the data in detail so that all golden answer information can be integrated. Then a feature-rich semantic representation is built for each word. Moreover, a simple but effective network is designed for question-aware representation for each document by captuing the interaction between questions and documents. The proposed model predicts answer text based on global representations of multiple candidate documents, leading to the runner-up position among 105 teams.
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2018, Vol. 32 
