@inproceedings{yang-etal-2017-character,
    title = "Character-level Intra Attention Network for Natural Language Inference",
    author = "Yang, Han  and
      Costa-juss{\`a}, Marta R.  and
      Fonollosa, Jos{\'e} A. R.",
    editor = "Bowman, Samuel  and
      Goldberg, Yoav  and
      Hill, Felix  and
      Lazaridou, Angeliki  and
      Levy, Omer  and
      Reichart, Roi  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 2nd Workshop on Evaluating Vector Space Representations for {NLP}",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/W17-5309/",
    doi = "10.18653/v1/W17-5309",
    pages = "46--50",
    abstract = "Natural language inference (NLI) is a central problem in language understanding. End-to-end artificial neural networks have reached state-of-the-art performance in NLI field recently. In this paper, we propose Character-level Intra Attention Network (CIAN) for the NLI task. In our model, we use the character-level convolutional network to replace the standard word embedding layer, and we use the intra attention to capture the intra-sentence semantics. The proposed CIAN model provides improved results based on a newly published MNLI corpus."
}