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