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This paper describes the system which got the state-of-the-art results atSemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. Inthis paper, we present a neural network called Hybrid Multi-Aspects (HMA)model, which mimic the human's intuitions on dealing with the multiple-choicereading comprehension. In this model, we aim to produce the predictions inmultiple aspects by calculating attention among the text, question and choices,and combine these results for final predictions. Experimental results show thatour HMA model could give substantial improvements over the baseline system andgot the first place on the final test set leaderboard with the accuracy of84.13%.

HFL-RC System at SemEval-2018 Task 11: Hybrid Multi-Aspects Model for Commonsense Reading Comprehension