A graph auto-encoder model of derivational morphology - Hofmann, V; Schütze, H; Pierrehumbert, JB (Association for Computational Linguistics, 2020)
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.