Using Disambiguated Word embeddings for Exploiting Similarities among Languages for Machine Translation
Neural distributed word representations have proven useful in Natural Language Processing and serve as the core underlying method for many NLP tasks. In this work we try to eliminate one of the shortcoming of word representation, by introducing disambiguated word representations and then extend them to build better translation dictionaries by exploiting the similarities between word representations of different languages. We experiment with different mapping functions (linear and nonlinear) for converting the word representations from one language to another. We test our approach by translating words from French to English. Our work is the direct extension of Rocher et al. [1] Mikolov et al. [2] and Trask et al. [3] Our method makes little assumption about the languages, and can be extended and improving translation tables for any language pairs.