Abstract
This paper proposes an approach for performing bilingual dictionary generation even when trained on widely available comparable bilingual corpora. We also show its capability to provide cross-lingual similarity estimates that correlate well with human judgments. We implement an approach using a nonlinear bilingual translation model that we train using comparable corpora. We propose a method using word embeddings and kernel approximation to train scalable nonlinear transformations. We demonstrate that this novel method works better on a majority of evaluated language pairs.
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