@InProceedings{vulic-mrkvsic-korhonen:2017:EMNLP2017,
  author    = {Vuli\'{c}, Ivan  and  Mrk\v{s}i\'{c}, Nikola  and  Korhonen, Anna},
  title     = {Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {2546--2558},
  abstract  = {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.},
  url       = {https://www.aclweb.org/anthology/D17-1270}
}