@inproceedings{mei-etal-2018-halo,
    title = "{H}alo: Learning Semantics-Aware Representations for Cross-Lingual Information Extraction",
    author = "Mei, Hongyuan  and
      Zhang, Sheng  and
      Duh, Kevin  and
      Van Durme, Benjamin",
    editor = "Nissim, Malvina  and
      Berant, Jonathan  and
      Lenci, Alessandro",
    booktitle = "Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/S18-2017/",
    doi = "10.18653/v1/S18-2017",
    pages = "142--147",
    abstract = "Cross-lingual information extraction (CLIE) is an important and challenging task, especially in low resource scenarios. To tackle this challenge, we propose a training method, called \textit{Halo}, which enforces the local region of each hidden state of a neural model to only generate target tokens with the same semantic structure tag. This simple but powerful technique enables a neural model to learn semantics-aware representations that are robust to noise, without introducing any extra parameter, thus yielding better generalization in both high and low resource settings."
}