Improving Low-Resource Machine Translation via Round-Trip Reinforcement Learning
This work addresses the problem of improving translation quality for low-resource languages, which is incremental as it builds on existing NLLB models with a novel fine-tuning approach.
The paper tackles low-resource machine translation by applying round-trip reinforcement learning fine-tuning to NLLB models, resulting in consistent improvements in translation quality for languages like Central Aymara and Wolof, with qualitative gains in fluency and semantic fidelity.
Low-resource machine translation (MT) has gained increasing attention as parallel data from low-resource language communities is collected, but many approaches for improving low-resource MT remain underexplored. We investigate a self-supervised reinforcement learning fine-tuning for translation in low-resource settings using round-trip bootstrapping with the No Language Left Behind (NLLB) family of models. Our approach translates English into a target low-resource language and then back into English, using a combination of chrF++ and BLEU as the reward function on the reconstructed English sentences. Using the NLLB-MD dataset, we evaluate both the 600M and 1.3B parameter NLLB models and observe consistent improvements for the following languages: Central Aymara, Friulian, Wolof, Dyula, Bhojpuri and Russian. Qualitative inspection of translation outputs indicates increased fluency and semantic fidelity. We argue that our method can further benefit from scale, enabling models to increasingly leverage their pretrained knowledge and continue self-improving. Code available at: https://github.com/Copticoder/MT-via-Round-Trip-RL