Translation of word vectors by exploiting translatable context pairs

While vector-based representations of word meanings (word vectors) have been widely used in a variety of natural language processing applications, they are not meant for capturing the similarity between words in different languages. This prevents using word vectors in multilingual-applications such as cross-lingual information retrieval and machine translation. To solve this problem, we propose a method that learns a cross-lingual projection of word representations from one language into another. Our method utilizes translatable context pairs obtained from a bilingual dictionary and surface similarity as bonus terms of the objective function. In the experiments, we evaluated the effectiveness of the proposed method in four languages, Japanese, Chinese, English and Spanish. Experiments shows that our method outperformed existing methods without any additional supervisions. © 2016, Japanese Society for Artificial Intelligence. All rights reserved.