用来回翻译强化学习，提升低资源翻译质量
Improving Low-Resource Machine Translation via Round-Trip Reinforcement Learning
- 通过中英双向翻译生成反馈信号，自监督优化模型
- 在6种低资源语言上实现流畅度与语义保真度提升
- 适合追求低成本、无标注数据的翻译研究者
低资源机器翻译受到越来越多关注，尽管来自低资源语言社区的平行语料不断积累，但相关改进方法仍待深入探索。本文提出一种基于回译自监督强化学习的微调方法，使用NLLB系列模型进行来回翻译，以chrF++和BLEU作为重建英文句子的奖励函数。在NLLB-MD数据集上，对600M和1.3B参数的NLLB模型进行评估，结果显示中央艾马拉语、弗留利语、沃洛夫语、迪尤拉语、博杰普里语和俄语均获得一致性能提升。定性分析显示翻译结果更流畅且语义更准确。我们认为该方法可随模型规模增长进一步受益，使模型持续利用预训练知识并自我优化。代码已开源：https://github.com/Copticoder/MT-via-Round-Trip-RL
原文摘要 · Abstract (English)
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
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