@inproceedings{farahnak-etal-2019-concordia,
    title = "The Concordia {NLG} Surface Realizer at {SRST} 2019",
    author = "Farahnak, Farhood  and
      Rafiee, Laya  and
      Kosseim, Leila  and
      Fevens, Thomas",
    editor = "Mille, Simon  and
      Belz, Anja  and
      Bohnet, Bernd  and
      Graham, Yvette  and
      Wanner, Leo",
    booktitle = "Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/D19-6308/",
    doi = "10.18653/v1/D19-6308",
    pages = "63--67",
    abstract = "This paper presents the model we developed for the shallow track of the 2019 NLG Surface Realization Shared Task. The model reconstructs sentences whose word order and word inflections were removed. We divided the problem into two sub-problems: reordering and inflecting. For the purpose of reordering, we used a pointer network integrated with a transformer model as its encoder-decoder modules. In order to generate the inflected forms of tokens, a Feed Forward Neural Network was employed."
}