VNJPTranslate: A comprehensive pipeline for Vietnamese-Japanese translation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911204976361472 |
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| author | Phan, Hoang Hai Vu, Nguyen Duc Minh Phuong, Nam Dang |
| author_facet | Phan, Hoang Hai Vu, Nguyen Duc Minh Phuong, Nam Dang |
| contents | Neural Machine Translation (NMT) driven by Transformer architectures has advanced significantly, yet faces challenges with low-resource language pairs like Vietnamese-Japanese (Vi-Ja). Issues include sparse parallel data and handling linguistic/cultural nuances. Recent progress in Large Language Models (LLMs) with strong reasoning, often refined via Reinforcement Learning (RL), enables high-quality synthetic data generation. We introduce VNJPTranslate, a pipeline designed to systematically address the Vi-Ja translation task. It features a targeted data augmentation strategy using advanced LLMs with Chain-of-Thought prompting for challenging segments identified via corpus analysis. Subsequently, we employ efficient fine-tuning techniques (Unsloth with QLoRA) on a capable, low-parameter autoregressive model (specifically, a fine-tuned version of the 1.8B parameter Sailor model, which is based on the Qwen architecture) to create a practical and high-performing translation system. This integrated approach aims to improve Vi-Ja translation quality significantly over existing baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_00339 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VNJPTranslate: A comprehensive pipeline for Vietnamese-Japanese translation Phan, Hoang Hai Vu, Nguyen Duc Minh Phuong, Nam Dang Computation and Language Artificial Intelligence Neural Machine Translation (NMT) driven by Transformer architectures has advanced significantly, yet faces challenges with low-resource language pairs like Vietnamese-Japanese (Vi-Ja). Issues include sparse parallel data and handling linguistic/cultural nuances. Recent progress in Large Language Models (LLMs) with strong reasoning, often refined via Reinforcement Learning (RL), enables high-quality synthetic data generation. We introduce VNJPTranslate, a pipeline designed to systematically address the Vi-Ja translation task. It features a targeted data augmentation strategy using advanced LLMs with Chain-of-Thought prompting for challenging segments identified via corpus analysis. Subsequently, we employ efficient fine-tuning techniques (Unsloth with QLoRA) on a capable, low-parameter autoregressive model (specifically, a fine-tuned version of the 1.8B parameter Sailor model, which is based on the Qwen architecture) to create a practical and high-performing translation system. This integrated approach aims to improve Vi-Ja translation quality significantly over existing baselines. |
| title | VNJPTranslate: A comprehensive pipeline for Vietnamese-Japanese translation |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2504.00339 |