Improving Word Embeddings via Combining with Complementary Languages

Word embeddings have recently been demonstrated outstanding results across various NLP tasks. However, most existing word embeddings learning methods employ mono-lingual corpus without exploiting the linguistic relationship among languages. In this paper, we introduce a novel CCL (Combination with Complementary Languages) method to improve word embeddings. Under this method, one word embeddings are replaced by its center word embeddings, which is obtained by combining with the corresponding word embeddings in other different languages. We apply our method to several baseline models and evaluate the quality of word embeddings on word similarity task across two benchmark datasets. Despite its simplicity, the results show that our method is surprisingly effective in capturing semantic information, and outperforms baselines by a large margin, at most 20 Spearman rank correlation (ρ×100).