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This paper presents our submissions to SemEval 2018 Task 12: the Argument Reasoning Comprehension Task. We investigate an end-to-end attention-based neural network to represent the two lexically close candidate warrants. On the one hand, we extract their different parts as attention vectors to obtain distinguishable representations. On the other hand, we use their surrounds (i.e., claim, reason, debate context) as another attention vectors to get contextual representations, which work as final clues to select the correct warrant. Our model achieves 60.4% accuracy and ranks 3rd among 22 participating systems.

ECNU at SemEval-2018 Task 12: An End-to-End Attention-based Neural Network for the Argument Reasoning Comprehension Task