@inproceedings{hofmann-etal-2020-graph,
    title = "A Graph Auto-encoder Model of Derivational Morphology",
    author = {Hofmann, Valentin  and
      Sch{\"u}tze, Hinrich  and
      Pierrehumbert, Janet},
    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/fix-sig-urls/2020.acl-main.106/",
    doi = "10.18653/v1/2020.acl-main.106",
    pages = "1127--1138",
    abstract = "There has been little work on modeling the morphological well-formedness (MWF) of derivatives, a problem judged to be complex and difficult in linguistics. We present a graph auto-encoder that learns embeddings capturing information about the compatibility of affixes and stems in derivation. The auto-encoder models MWF in English surprisingly well by combining syntactic and semantic information with associative information from the mental lexicon."
}