@inproceedings{kim-etal-2018-snu,
    title = "{SNU}{\_}{IDS} at {S}em{E}val-2018 Task 12: Sentence Encoder with Contextualized Vectors for Argument Reasoning Comprehension",
    author = "Kim, Taeuk  and
      Choi, Jihun  and
      Lee, Sang-goo",
    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/add-emnlp-2024-awards/S18-1182/",
    doi = "10.18653/v1/S18-1182",
    pages = "1083--1088",
    abstract = "We present a novel neural architecture for the Argument Reasoning Comprehension task of SemEval 2018. It is a simple neural network consisting of three parts, collectively judging whether the logic built on a set of given sentences (a claim, reason, and warrant) is plausible or not. The model utilizes contextualized word vectors pre-trained on large machine translation (MT) datasets as a form of transfer learning, which can help to mitigate the lack of training data. Quantitative analysis shows that simply leveraging LSTMs trained on MT datasets outperforms several baselines and non-transferred models, achieving accuracies of about 70{\%} on the development set and about 60{\%} on the test set."
}