Bilingual Word-Embedding for Korean and English Without Word Alignments

Abstract

In spite of lots of cross-lingual word embedding models for various languages, approaches that support cross-lingual word embedding between languages that have different word order and different origin word are lacking. In this study, we address the problem of cross-lingual word embedding between Korean and English that have different word order and origin and perform experiments to examine its performance behavior. Cross-lingual models have different levels of supervision. For training between languages which have different word order, it is essential to reduce preprocessing time. Therefore, two sentence-level alignment cross-lingual models are chosen for our experiments. Our results show that cross-lingual embedding for Korean and English without word-alignment is possible. We also analyze which bilingual tasks are proper for each trained result by comparing characteristic of each model’s trained result.