Parallel sentences mining with transfer learning in an unsupervised setting
Yu Sun, Shaolin Zhu, Yifan Feng, Chenggang Mi · 2021
The quality and quantity of parallel sentences are known as very important training data for constructing neural machine translation (N-MT) systems.However, these resources are not available for many low-resource language pairs.Many existing methods need strong supervision and hence are not suitable.Although there have been several attempts at developing unsupervised models, they ignore the language-invariant between languages.In this paper, we propose an approach based on transfer learning to mine parallel sentences in an unsupervised setting.With the help of bilingual corpora of rich-resource language pairs, we can mine parallel sentences without bilingual supervision of low-resource language pairs.Experiments show that our approach improves the performance of mined parallel sentences compared with previous methods.In particular, we achieve good results at two real-world low-resource language pairs.