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Existing approaches to automatic VerbNet-style verb classification areheavily dependent on feature engineering and therefore limited to languageswith mature NLP pipelines. In this work, we propose a novel cross-lingualtransfer method for inducing VerbNets for multiple languages. To the best ofour knowledge, this is the first study which demonstrates how the architecturesfor learning word embeddings can be applied to this challengingsyntactic-semantic task. Our method uses cross-lingual translation pairs to tieeach of the six target languages into a bilingual vector space with English,jointly specialising the representations to encode the relational informationfrom English VerbNet. A standard clustering algorithm is then run on top of theVerbNet-specialised representations, using vector dimensions as features forlearning verb classes. Our results show that the proposed cross-lingualtransfer approach sets new state-of-the-art verb classification performanceacross all six target languages explored in this work.

Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation