  We present Attract-Repel, an algorithm for improving the semantic quality of
word vectors by injecting constraints extracted from lexical resources.
Attract-Repel facilitates the use of constraints from mono- and cross-lingual
resources, yielding semantically specialised cross-lingual vector spaces. Our
evaluation shows that the method can make use of existing cross-lingual
lexicons to construct high-quality vector spaces for a plethora of different
languages, facilitating semantic transfer from high- to lower-resource ones.
The effectiveness of our approach is demonstrated with state-of-the-art results
on semantic similarity datasets in six languages. We next show that
Attract-Repel-specialised vectors boost performance in the downstream task of
dialogue state tracking (DST) across multiple languages. Finally, we show that
cross-lingual vector spaces produced by our algorithm facilitate the training
of multilingual DST models, which brings further performance improvements.
