Towards the Next 1000 Languages in Multilingual Machine Translation: Exploring the Synergy Between Supervised and Self-Supervised Learning

• URL: http://arxiv.org/abs/2201.03110v2
• Date: Thu, 13 Jan 2022 18:09:08 GMT
• Title: Towards the Next 1000 Languages in Multilingual Machine Translation: Exploring the Synergy Between Supervised and Self-Supervised Learning
• Authors: Aditya Siddhant, Ankur Bapna, Orhan Firat, Yuan Cao, Mia Xu Chen, Isaac Caswell, Xavier Garcia
• Abstract summary: We present a pragmatic approach towards building a multilingual machine translation model that covers hundreds of languages. We use a mixture of supervised and self-supervised objectives, depending on the data availability for different language pairs. We demonstrate that the synergy between these two training paradigms enables the model to produce high-quality translations in the zero-resource setting.
• Score: 48.15259834021655
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Achieving universal translation between all human language pairs is the holy-grail of machine translation (MT) research. While recent progress in massively multilingual MT is one step closer to reaching this goal, it is becoming evident that extending a multilingual MT system simply by training on more parallel data is unscalable, since the availability of labeled data for low-resource and non-English-centric language pairs is forbiddingly limited. To this end, we present a pragmatic approach towards building a multilingual MT model that covers hundreds of languages, using a mixture of supervised and self-supervised objectives, depending on the data availability for different language pairs. We demonstrate that the synergy between these two training paradigms enables the model to produce high-quality translations in the zero-resource setting, even surpassing supervised translation quality for low- and mid-resource languages. We conduct a wide array of experiments to understand the effect of the degree of multilingual supervision, domain mismatches and amounts of parallel and monolingual data on the quality of our self-supervised multilingual models. To demonstrate the scalability of the approach, we train models with over 200 languages and demonstrate high performance on zero-resource translation on several previously under-studied languages. We hope our findings will serve as a stepping stone towards enabling translation for the next thousand languages.

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