A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation

Anonymous

Paper Link: https://openreview.net/forum?id=EtZ9h4Lqs5-

Paper Type: Long paper (up to eight pages of content + unlimited references and appendices)

Abstract: Recent advances in the pre-training for language models leverage large-scale datasets to create multilingual models. However, low-resource languages are mostly left out in these datasets. This is primarily because many widely spoken languages that are not well represented on the web and therefore excluded from the large-scale crawls for datasets. Furthermore, downstream users of these models are restricted to the selection of languages originally chosen for pre-training. This work investigates how to optimally leverage existing pre-trained models to create low-resource translation systems for 16 African languages. We focus on two questions: 1) How can pre-trained models be used for languages not included in the initial pretraining? and 2) How can the resulting translation models effectively transfer to new domains? To answer these questions, we create a novel African news corpus covering 16 languages, of which eight languages are not part of any existing evaluation dataset. We demonstrate that the most effective strategy for transferring both additional languages and additional domains is to leverage small quantities of high-quality translation data to fine-tune large pre-trained models.

Presentation Mode: This paper will be presented in person in Seattle

Copyright Consent Signature (type Name Or NA If Not Transferrable): David Ifeoluwa Adelani

Copyright Consent Job Title: PhD Researcher

Copyright Consent Name And Address: Saarland university, 66123, Saarbruecken, Germany