Bilingual Learning of Multi-sense Embeddings with Discrete Autoencoders
Simon Šuster, Ivan S. Titov, Gertjan van Noord · 2016
We present an approach to learning multi-sense word embeddings relying both on monolingual and bilingual information.Our model consists of an encoder, which uses monolingual and bilingual context (i.e. a parallel sentence) to choose a sense for a given word, and a decoder which predicts context words based on the chosen sense.The two components are estimated jointly.We observe that the word representations induced from bilingual data outperform the monolingual counterparts across a range of evaluation tasks, even though crosslingual information is not available at test time.