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This shared task is a typical question answering (QA) task. Specially, this task must give the answer to the question based on the text provided. The essence of the problem is actually reading comprehension. For each question, there are two candidate answers, and only one of them is correct. Existing method for this task is to use convolutional neural network (CNN) and recurrent neural network (RNN) or their improved models, such as long short-term memory (LSTM). In this paper, an attention-based CNN-LSTM model is proposed for this task. By adding an attention mechanism and combining the two models, the experimental results have been significantly improved. The accuracy of our final submission is 0.7143.

YNU-HPCC at Semeval-2018 Task 11: Using an Attention-based CNN-LSTM for Machine Comprehension using Commonsense Knowledge