@inproceedings{luotolahti-etal-2017-cross,
    title = "Cross-Lingual Pronoun Prediction with Deep Recurrent Neural Networks v2.0",
    author = "Luotolahti, Juhani  and
      Kanerva, Jenna  and
      Ginter, Filip",
    editor = {Webber, Bonnie  and
      Popescu-Belis, Andrei  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the Third Workshop on Discourse in Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/W17-4808/",
    doi = "10.18653/v1/W17-4808",
    pages = "63--66",
    abstract = "In this paper we present our system in the DiscoMT 2017 Shared Task on Crosslingual Pronoun Prediction. Our entry builds on our last year`s success, our system based on deep recurrent neural networks outperformed all the other systems with a clear margin. This year we investigate whether different pre-trained word embeddings can be used to improve the neural systems, and whether the recently published Gated Convolutions outperform the Gated Recurrent Units used last year."
}