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About this paper
Tomas Mikolov, Quoc V. Le, Ilya Sutskever
Researchers from Google Inc. demonstrated a simple method for generating and extending machine translation dictionaries by learning linear transformations between independently trained word embedding spaces of different languages. This approach achieved almost 90% Precision@5 for English-Spanish word translation, significantly outperforming traditional methods.
Key insights
- Word vector spaces, trained independently for different languages, preserve similar semantic and syntactic relationships, forming geometrically analogous structures.
- A basic linear transformation can effectively map word vectors from one language's embedding space to another, enabling cross-lingual lexical inference.
- Accurate word translation can be achieved largely using readily available monolingual data, needing only a small bilingual dictionary for alignment.
Summary by alphaXiv.
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