Improving Low-Resource Machine Translation via Round-Trip Reinforcement Learning

• URL: http://arxiv.org/abs/2601.12535v1
• Date: Sun, 18 Jan 2026 18:44:49 GMT
• Title: Improving Low-Resource Machine Translation via Round-Trip Reinforcement Learning
• Authors: Ahmed Attia, Alham Fikri,
• Abstract summary: We investigate a self-supervised reinforcement-learning-based fine-tuning for translation in low-resource settings.<n>Our approach translates English into a target low-resource language and then back into English, using a combination of chrF++ and BLEU.<n>We observe consistent improvements for the following languages: Central Aymara, Friulian, Wolof and Russian.
• Score: 0.36398711296758063
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Low-resource machine translation (MT) has gained increasing attention as parallel data from low-resource language communities is collected, but many potential methods for improving low-resource MT remain unexplored. We investigate a self-supervised reinforcement-learning-based 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 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.

Related papers

• Beyond Many-Shot Translation: Scaling In-Context Demonstrations For Low-Resource Machine Translation [49.82863380286994]
  In-context learning may offer novel ways to adapt Large Language Models for low-resource machine translation.<n>In this study, we explore scaling low-resource machine translation ICL beyond the few-shot setting to thousands of examples with long-context models.<n>Our experiments on Javanese and Sundanese show that gains from additional context saturate quickly and can degrade near the maximum context window.
  arXiv  Detail & Related papers  (2026-02-04T17:02:22Z)
• Improving Low-Resource Translation with Dictionary-Guided Fine-Tuning and RL: A Spanish-to-Wayuunaiki Study [0.27998963147546146]
  We propose a novel approach that enhances translation for low-resource languages by integrating an external dictionary tool and training models end-to-end.<n>We frame translation as a tool-augmented decision-making problem in which the model can selectively consult a bilingual dictionary during generation.<n>Preliminary results show that our tool-augmented models achieve up to +3.37 BLEU improvement over previous work.
  arXiv  Detail & Related papers  (2025-08-26T23:50:55Z)
• Data Augmentation With Back translation for Low Resource languages: A case of English and Luganda [0.0]
  We explore the application of Back translation as a semi-supervised technique to enhance Neural Machine Translation models for the English-Luganda language pair.<n>Our methodology involves developing custom NMT models using both publicly available and web-crawled data, and applying Iterative and Incremental Back translation techniques.<n>The results of our study show significant improvements, with translation performance for the English-Luganda pair exceeding previous benchmarks by more than 10 BLEU score units across all translation directions.
  arXiv  Detail & Related papers  (2025-05-05T08:47:52Z)
• Is Small Language Model the Silver Bullet to Low-Resource Languages Machine Translation? [18.975160361440597]
  Low-resource languages (LRLs) lack sufficient linguistic resources and are underrepresented in benchmark datasets.<n>This study systematically evaluates state-of-the-art smaller Large Language Models in 200 languages.
  arXiv  Detail & Related papers  (2025-03-31T13:56:03Z)
• Understanding In-Context Machine Translation for Low-Resource Languages: A Case Study on Manchu [53.437954702561065]
  In-context machine translation (MT) with large language models (LLMs) is a promising approach for low-resource MT.<n>This study systematically investigates how each type of resource, e.g., dictionary, grammar book, and retrieved parallel examples, affect the translation performance.<n>Our results indicate that high-quality dictionaries and good parallel examples are very helpful, while grammars hardly help.
  arXiv  Detail & Related papers  (2025-02-17T14:53:49Z)
• Challenges in Adapting Multilingual LLMs to Low-Resource Languages using LoRA PEFT Tuning [0.4194295877935868]
  This study investigates the effects of Low-Rank Adaptation (LoRA) -Efficient Fine-Tuning (PEFT) on multilingual Gemma models for Marathi.<n>Using a translated dataset with 52,000 instruction-response pairs, our findings reveal that while evaluation performance decline post-fine-tuning, manual assessments frequently suggest that the fine-tuned models outperform their original counterparts.
  arXiv  Detail & Related papers  (2024-11-27T18:14:38Z)
• Unlocking the Potential of Model Merging for Low-Resource Languages [66.7716891808697]
  Adapting large language models to new languages typically involves continual pre-training (CT) followed by supervised fine-tuning (SFT) We propose model merging as an alternative for low-resource languages, combining models with distinct capabilities into a single model without additional training. Experiments based on Llama-2-7B demonstrate that model merging effectively endows LLMs for low-resource languages with task-solving abilities, outperforming CT-then-SFT in scenarios with extremely scarce data.
  arXiv  Detail & Related papers  (2024-07-04T15:14:17Z)
• Democratizing LLMs for Low-Resource Languages by Leveraging their English Dominant Abilities with Linguistically-Diverse Prompts [75.33019401706188]
  Large language models (LLMs) are known to effectively perform tasks by simply observing few exemplars. We propose to assemble synthetic exemplars from a diverse set of high-resource languages to prompt the LLMs to translate from any language into English. Our unsupervised prompting method performs on par with supervised few-shot learning in LLMs of different sizes for translations between English and 13 Indic and 21 African low-resource languages.
  arXiv  Detail & Related papers  (2023-06-20T08:27:47Z)
• Strategies for improving low resource speech to text translation relying on pre-trained ASR models [59.90106959717875]
  This paper presents techniques and findings for improving the performance of low-resource speech to text translation (ST) We conducted experiments on both simulated and real-low resource setups, on language pairs English - Portuguese, and Tamasheq - French respectively.
  arXiv  Detail & Related papers  (2023-05-31T21:58:07Z)
• Semi-supervised Neural Machine Translation with Consistency Regularization for Low-Resource Languages [3.475371300689165]
  This paper presents a simple yet effective method to tackle the problem for low-resource languages by augmenting high-quality sentence pairs and training NMT models in a semi-supervised manner. Specifically, our approach combines the cross-entropy loss for supervised learning with KL Divergence for unsupervised fashion given pseudo and augmented target sentences. Experimental results show that our approach significantly improves NMT baselines, especially on low-resource datasets with 0.46--2.03 BLEU scores.
  arXiv  Detail & Related papers  (2023-04-02T15:24:08Z)
• Continual Mixed-Language Pre-Training for Extremely Low-Resource Neural Machine Translation [53.22775597051498]
  We present a continual pre-training framework on mBART to effectively adapt it to unseen languages. Results show that our method can consistently improve the fine-tuning performance upon the mBART baseline. Our approach also boosts the performance on translation pairs where both languages are seen in the original mBART's pre-training.
  arXiv  Detail & Related papers  (2021-05-09T14:49:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.