Low-Resource Machine Translation Training Curriculum Fit for Low-Resource Languages

TL;DR

This paper introduces a low-resource NMT training curriculum that leverages weak supervision and modest compute resources, significantly improving translation quality for underrepresented languages.

Contribution

The paper proposes a novel training curriculum combining weak supervision, back-translation, and auto-encoding for low-resource languages, achieving state-of-the-art results.

Findings

BLEU scores improved by +12.2 for English-Gujarati

Achieved new state-of-the-art BLEU of 29.3 for Somali-English

Adding compute resources further enhances translation performance

Abstract

We conduct an empirical study of neural machine translation (NMT) for truly low-resource languages, and propose a training curriculum fit for cases when both parallel training data and compute resource are lacking, reflecting the reality of most of the world's languages and the researchers working on these languages. Previously, unsupervised NMT, which employs back-translation (BT) and auto-encoding (AE) tasks has been shown barren for low-resource languages. We demonstrate that leveraging comparable data and code-switching as weak supervision, combined with BT and AE objectives, result in remarkable improvements for low-resource languages even when using only modest compute resources. The training curriculum proposed in this work achieves BLEU scores that improve over supervised NMT trained on the same backbone architecture by +12.2 BLEU for English to Gujarati and +3.7 BLEU for English to Kazakh, showcasing the potential of weakly-supervised NMT for the low-resource languages. When trained on supervised data, our training curriculum achieves a new state-of-the-art result on the Somali dataset (BLEU of 29.3 for Somali to English). We also observe that adding more time and GPUs to training can further improve performance, which underscores the importance of reporting compute resource usage in MT research.