Multiligual machine translation aims at learning a single tanslation model for multiple languages. However, high resource language often suffers from performance degradation. In this blog, we present a method LaSS proposed in a recent ACL paper on multilingual neural machine translation. The LaSS is an approach to jointly train a single unified multilingual MT model and learns language-specific subnetwork for each language pair. Authors conducted experiments on IWSLT and WMT datasets with various Transformer architectures. The experimental results demonstrates average 1.2 BLEU improvements on 36 language pairs. LaSS shows strong generalization capabilty and demonstrates strong performance in zero-shot translation. Specifically, LaSS achieves 8.3 BLEU on 30 language pairs.