Following their monolingual counterparts, bilingual word embeddings are also on the rise. As a major application task, word translation has been relying on the nearest neighbor to connect embeddings cross-lingually. However, the nearest neighbor strategy suffers from its inherently local nature and fails to cope with variations in realistic bilingual word embeddings. Furthermore, it lacks a mechanism to deal with many-to-many mappings that often show up across languages. We introduce Earth Mover's Distance to this task by providing a natural formulation that translates words in a holistic fashion, addressing the limitations of the nearest neighbor. We further extend the formulation to a new task of identifying parallel sentences, which is useful for statistical machine translation systems, thereby expanding the application realm of bilingual word embeddings. We show encouraging performance on both tasks.

Patent summaries are machine-translated using bilingual term entries extracted from parallel texts for evaluation. The result shows that bilingual term entries extracted from 2,000 pairs of parallel texts which share a specific domain with the input texts introduce more improvements than a technical term dictionary with 38,000 entries which covers a broader domain. The result also shows that only 10 pairs of parallel texts found by similar document retrieval have comparable effects to the technical term dictionary, suggesting that parallel texts to be used do not need to be classified into fields prior to term extraction.