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Hofmann, Valentin; Schütze, Hinrich; Pierrehumbert, Janet B. (2020): A Graph Auto-encoder Model of Derivational Morphology. In: 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020): pp. 1127-1138
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There has been little work on modeling the morphological well-formedness (MWF) of derivatives, a problem judged to be complex and difficult in linguistics (Bauer, 2019). 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.