Common intermediate language representation in neural machine translation can be used to extend bilingual to multilingual systems by incremental training. In this paper, we propose a new architecture based on introducing an interlingual loss as an additional training objective. By adding and forcing this interlingual loss, we are able to train multiple encoders and decoders for each language, sharing a common intermediate representation. Translation results on the low-resourced tasks (Turkish-English and Kazakh-English tasks, from the popular Workshop on Machine Translation benchmark) show the following BLEU improvements up to 2.8. However, results on a larger dataset (Russian-English and Kazakh-English, from the same baselines) show BLEU loses if the same amount. While our system is only providing improvements for the low-resourced tasks in terms of translation quality, our system is capable of quickly deploying new language pairs without retraining the rest of the system, which may be a game-changer in some situations (i.e. in a disaster crisis where international help is required towards a small region or to develop some translation system for a client). Precisely, what is most relevant from our architecture is that it is capable of: (1) reducing the number of production systems, with respect to the number of languages, from quadratic to linear (2) incrementally adding a new language in the system without retraining languages previously there and (3) allowing for translations from the new language to all the others present in the system
翻译:神经机翻译中常见中间语言代表制可以通过渐进式培训将双语推广到多语言系统。 在本文中,我们提出一个基于引入一种语言间损失的新架构,作为额外的培训目标。通过添加和强制这种语言间损失,我们能够为每种语言培训多种编码器和解码器,共享一个共同的中间代表制。低资源任务(土耳其-英语和哈萨克-英语任务,来自流行的机器翻译基准讲习班)的翻译结果显示以下BLEU的改进达2.8。然而,一个更大的数据集(俄语-英语和哈萨克语-英语,来自同一基线)的结果显示BLEU的亏损,如果数额相同。虽然我们的系统只能为低资源翻译质量方面的任务提供改进,但我们的系统能够迅速部署新语言配对,而不必再培训系统的其他部分,在某些情况下(例如,在灾难危机中,需要对一个小区域提供国际援助或为客户开发某种翻译系统)的翻译结果显示BLEEUEU值。 准确地说,我们的系统最相关的是,它能够从一个不断更新的语文数量,从一个系统到另一个语言的版本,从一个不断的翻译到另一个语言的版本,从一个系统,从一个不断的版本到另一个的版本的版本到另一个的版本。