@inproceedings{xu-etal-2018-cross,
    title = "Cross-Target Stance Classification with Self-Attention Networks",
    author = "Xu, Chang  and
      Paris, C{\'e}cile  and
      Nepal, Surya  and
      Sparks, Ross",
    editor = "Gurevych, Iryna  and
      Miyao, Yusuke",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/P18-2123/",
    doi = "10.18653/v1/P18-2123",
    pages = "778--783",
    abstract = "In stance classification, the target on which the stance is made defines the boundary of the task, and a classifier is usually trained for prediction on the same target. In this work, we explore the potential for generalizing classifiers between different targets, and propose a neural model that can apply what has been learned from a source target to a destination target. We show that our model can find useful information shared between relevant targets which improves generalization in certain scenarios."
}