@inproceedings{marzinotto-2020-framenet,
    title = "{F}rame{N}et Annotations Alignment using Attention-based Machine Translation",
    author = "Marzinotto, Gabriel",
    editor = "Torrent, Tiago T.  and
      Baker, Collin F.  and
      Czulo, Oliver  and
      Ohara, Kyoko  and
      Petruck, Miriam R. L.",
    booktitle = "Proceedings of the International FrameNet Workshop 2020: Towards a Global, Multilingual FrameNet",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.framenet-1.6/",
    pages = "41--47",
    language = "eng",
    ISBN = "979-10-95546-58-0",
    abstract = "This paper presents an approach to project FrameNet annotations into other languages using attention-based neural machine translation (NMT) models. The idea is to use an NMT encoder-decoder attention matrix to propose a word-to-word correspondence between the source and the target language. We combine this word alignment along with a set of simple rules to securely project the FrameNet annotations into the target language. We successfully implemented, evaluated and analyzed this technique on the English-to-French configuration. First, we analyze the obtained FrameNet lexicon qualitatively. Then, we use existing French FrameNet corpora to assert the quality of the translation. Finally, we trained a BERT-based FrameNet parser using the projected annotations and compared it to a BERT baseline. Results show substantial improvements in the French language, giving evidence to support that our approach could help to propagate FrameNet data-set on other languages."
}