Most translation tasks among languages belong to the zero-resource translation problem where parallel corpora are unavailable. Multilingual neural machine translation (MNMT) enables one-pass translation using shared semantic space for all languages compared to the two-pass pivot translation but often underperforms the pivot-based method. In this paper, we propose a novel method, named as Unified Multilingual Multiple teacher-student Model for NMT (UM4). Our method unifies source-teacher, target-teacher, and pivot-teacher models to guide the student model for the zero-resource translation. The source teacher and target teacher force the student to learn the direct source to target translation by the distilled knowledge on both source and target sides. The monolingual corpus is further leveraged by the pivot-teacher model to enhance the student model. Experimental results demonstrate that our model of 72 directions significantly outperforms previous methods on the WMT benchmark.
翻译:语言之间大多数翻译任务都属于零资源翻译问题,因为没有平行的翻译。多语言神经机翻译(MNMT)使得所有语言使用共享的语义空间进行一次性翻译,与双通对流翻译相比,所有语言使用共享的语义空间,但往往不完善以支流为基础的方法。在本文中,我们提出了一个新颖的方法,名为NMT(UM4)多语种统一多语种教师-学生学生模式(UM4)。我们的方法统一了源教师、目标教师和支流教师模式,以指导零资源翻译的学生模式。源教师和目标教师迫使学生学习直接来源,学习源和目标两侧的精选知识,以瞄准翻译的目标。单语种软件被Pivat教师模式进一步用于加强学生模式。实验结果表明,我们的72个方向模式大大超越了WMT基准的以往方法。