How to develop a single unified model to translate from any language to any language? This work proposes a many-to-many translation system with emphasis on both English-centric and non-English directions. Many recent works have focused on proposing a single unified model for multiligual translation. These models are favorable because they are efficient and easy for deployment. However, most of these works focus on improving English-centric directions, which means that translation between two arbitrary languages may not be well supported. Therefore, in this paper, they propose a training method called mRASP2, including contrastive learning and alignment augmentation (AA) to train a unified multilingual translation system. They also contribute a monolingual dataset called MC24. By making use of monolingual and bilingual language copora, the system is able to learn language-agnostic representation to support non-English directions better than before. Their system achieves great performances and outperforms a strong Transformer baseline by a large margin.

​ In 1920, the great philosopher Bertrand Russell visited China, accompanied by Yuen Ren Chao, a Chinese-American linguist. Mr. Chao was a naturally gifted polyglot. At that time, he could already speak Baoding dialect, Wu dialect, Fuzhou dialect, Nanjing dialect, and English. He accompanied Russell from Shanghai to Changsha by ship. During the trip, he was learning Changsha dialect from Yang Ruiliu, an economist on the same ship. When the ship docked in Changsha, Yuen Ren Chao was already able to translate Russell's speeches and slang into Changsha dialect. Can our neural network become a model like "Yuen Ren Chao" on machine translation? That is, to create a unified model with multilingual abilities, and when encountering new languages, the model could quickly adapt to translating new ones after training with a small amount of data.