@inproceedings{augenstein-etal-2018-multi,
    title = "Multi-Task Learning of Pairwise Sequence Classification Tasks over Disparate Label Spaces",
    author = "Augenstein, Isabelle  and
      Ruder, Sebastian  and
      S{\o}gaard, Anders",
    editor = "Walker, Marilyn  and
      Ji, Heng  and
      Stent, Amanda",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/N18-1172/",
    doi = "10.18653/v1/N18-1172",
    pages = "1896--1906",
    abstract = "We combine multi-task learning and semi-supervised learning by inducing a joint embedding space between disparate label spaces and learning transfer functions between label embeddings, enabling us to jointly leverage unlabelled data and auxiliary, annotated datasets. We evaluate our approach on a variety of tasks with disparate label spaces. We outperform strong single and multi-task baselines and achieve a new state of the art for aspect-based and topic-based sentiment analysis."
}