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Paper detail

Machine Translation with Cross-lingual Word Embeddings

Learning word embeddings using distributional information is a task that has been studied by many researchers, and a lot of studies are reported in the literature. On the contrary, less studies were done for the case of multiple languages. The idea is to focus on a single representation for a pair of languages such that semantically similar words are closer to one another in the induced representation irrespective of the language. In this way, when data are missing for a particular language, classifiers from another language can be used.

preprint2020arXivOpen access
Marco BerlotEvan Kaplan
Computation and LanguageMachine Learning
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Marco BerlotEvan Kaplan

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