Joint word2vec Networks for Bilingual Semantic Representations

Abstract

We extend the word2vec framework to capture meaning across languages. The input consists of a source text and a word-aligned parallel text in a second language. The joint word2vec tool then represents words in both languages within a common “semantic” vector space. The result can be used to enrich lexicons of under-resourced languages, to identify ambiguities, and to perform clustering and classification. Experiments were conducted on a parallel English-Arabic corpus, as well as on English and Hebrew Biblical texts.