A Novel Deep Learning Method for Obtaining Bilingual Corpus from Multilingual Website
Shaolin Zhu, Li Xiao, Yating Yang, Lei Wang, Chenggang Mi · Mathematical Problems in Engineering · 2019
Machine translation needs a large number of parallel sentence pairs to make sure of having a good translation performance. However, the lack of parallel corpus heavily limits machine translation for low‐resources language pairs. We propose a novel method that combines the continuous word embeddings with deep learning to obtain parallel sentences. Since parallel sentences are very invaluable for low‐resources language pair, we introduce cross‐lingual semantic representation to induce bilingual signals. Our experiments show that we can achieve promising results under lacking external resources for low‐resource languages. Finally, we construct a state‐of‐the‐art machine translation system in low‐resources language pair.