@inproceedings{tian-etal-2018-ecnu,
    title = "{ECNU} at {S}em{E}val-2018 Task 12: An End-to-End Attention-based Neural Network for the Argument Reasoning Comprehension Task",
    author = "Tian, Junfeng  and
      Lan, Man  and
      Wu, Yuanbin",
    editor = "Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      May, Jonathan  and
      Shutova, Ekaterina  and
      Bethard, Steven  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/S18-1184/",
    doi = "10.18653/v1/S18-1184",
    pages = "1094--1098",
    abstract = "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."
}