Enhancing distant low-resource neural machine translation with semantic pivot

Abstract

Prior work has proved that pivot-based method can boost the performance of neural machine translation (NMT). However, in low-resource scenarios, the efficient of pivot-based method is impaired severely due to data sparsity problem. As a typical low-resource language pair, Chinese-Lao NMT suffers the same performance dilemma. In addition, due to the significant linguistic gap between Chinese and Lao, some traditional and effective low-resource translation methods, such as introducing similarity external knowledge, sharing word space, and literal translation, are not suitable for the translation of this language pair. Fortunately, it is highly adaptable to pivot strategy, as there is a pivot language, Thai, which is highly similar to the target language Lao. Here, we propose a novel approach for incorporating similar linguistic features between Thai and Lao into the Chinese-Lao translation model. Firstly, an in-depth linguistic similarity analysis of Thai and Lao is conducted. Secondly, an elaborate pivot-based translation framework with KL adapter is applied. Experiments on the Chinese-Lao translation task show that our approach can help transfer more linguistic knowledges from the Chinese encoder to the Lao decoder via similar linguistic features, achieving substantial improvements compared to the baseline models.