@inproceedings{weller-di-marco-fraser-2020-modeling,
    title = "Modeling Word Formation in {E}nglish{--}{G}erman Neural Machine Translation",
    author = "Weller-Di Marco, Marion  and
      Fraser, Alexander",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2020.acl-main.389/",
    doi = "10.18653/v1/2020.acl-main.389",
    pages = "4227--4232",
    abstract = "This paper studies strategies to model word formation in NMT using rich linguistic information, namely a word segmentation approach that goes beyond splitting into substrings by considering fusional morphology. Our linguistically sound segmentation is combined with a method for target-side inflection to accommodate modeling word formation. The best system variants employ source-side morphological analysis and model complex target-side words, improving over a standard system."
}