  Acquisition of multilingual training data continues to be a challenge in word
sense disambiguation (WSD). To address this problem, unsupervised approaches
have been proposed to automatically generate sense annotations for training
supervised WSD systems. We present three new methods for creating
sense-annotated corpora which leverage translations, parallel bitexts, lexical
resources, as well as contextual and synset embeddings. Our semi-supervised
method applies machine translation to transfer existing sense annotations to
other languages. Our two unsupervised methods refine sense annotations produced
by a knowledge-based WSD system via lexical translations in a parallel corpus.
We obtain state-of-the-art results on standard WSD benchmarks.
