Building Machine Translation Systems for the Next Thousand Languages
This work addresses the challenge of machine translation for under-served and understudied languages, providing insights for practitioners, though it is incremental in building on existing multilingual models.
The paper tackled the problem of building practical machine translation systems for over one thousand languages, achieving this by creating clean datasets for 1500+ languages and developing models that leverage supervised data for 100+ high-resource languages and monolingual data for 1000+ more, while also analyzing evaluation metrics and error modes.
In this paper we share findings from our effort to build practical machine translation (MT) systems capable of translating across over one thousand languages. We describe results in three research domains: (i) Building clean, web-mined datasets for 1500+ languages by leveraging semi-supervised pre-training for language identification and developing data-driven filtering techniques; (ii) Developing practical MT models for under-served languages by leveraging massively multilingual models trained with supervised parallel data for over 100 high-resource languages and monolingual datasets for an additional 1000+ languages; and (iii) Studying the limitations of evaluation metrics for these languages and conducting qualitative analysis of the outputs from our MT models, highlighting several frequent error modes of these types of models. We hope that our work provides useful insights to practitioners working towards building MT systems for currently understudied languages, and highlights research directions that can complement the weaknesses of massively multilingual models in data-sparse settings.