Jul 2026· Annual International ACM SIGIR Conference on Research and Development in Information Retrieval· 0 citations· 19 references
Computer Science
Abstract
Machine translation for low-resource languages suffers from domain-imbalanced corpora, causing quality degradation on technical text. However, in-context learning opens the possibility to rely on limited in-domain corpora to inform translation. We present lessons learned from Tulun, a retrieval-augmented system combining neural MT with LLM post-editing, guided by user-configurable translation memories and glossaries. Deployed for medical translation in Timor-Leste (Tetun) and disaster relief translation in Vanuatu (Bislama), the system achieves accuracy improvements over baseline MT by 16.90-22.41 ChrF++ points, while offering rapid adaptability and transparency to end-users. Key recommendations include: domain granularity matters more than broad categories; translation target audience should inform retrieval; and RAG-augmented MT is most effective for languages that lack domain corpora but remain within LLM pretraining distributions.
Large language models continue to face challenges in translating low-resource languages with scarce parallel data. This study investigates how to fine-tune them effectively using target-side monolingual data. Existing approaches—dominated by back-translation and recent LLM-based rewriting—remain limited by noisy synthetic sources, unguided simplification, and the absence of a principled mechanism for integrating monolingual sentences into the training objective. To address this, we developed a semi-supervised framework that integrates marginal distribution estimation and curriculum-guided rewriting to exploit monolingual data for low-resource translation. Experiments in four low-resource directions demonstrated substantial gains, averaging +8 spBLEU and +10 COMET over strong baselines, while three additional mid-resource directions showed stable improvements and consistent trends. Reference-free metrics further validated robust gains in fluency and adequacy. The findings establish a scalable paradigm for low-resource translation, revealing that the principled integration of marginal likelihood estimation and generative rewriting enables large language models to achieve superior performance under extreme data scarcity.
Wenjie Yu, Zhiqiang Yu, Zuo Jiang et al.· ACM Transactions on Asian an...· 0 citations
The retrieval-augmented many-shot translation pipeline from the AmericasNLP 2026 system is adapted to translate between English and eleven North-Eastern Indian languages in both directions to solve the WMT26 Low-Resource Indic Language Translation shared task.
Aashish Dhawan, Christopher Driggers-Ellis, Dzmitry Kasinets et al.· 0 citations
Data-limited multi-domain machine translation remains challenging because parallel corpora are scarce in specialized domains, domain terminology is highly constrained, and large language models may generate fluent but unfaithful translations. Direct prompting or ordinary fine-tuning is therefore insufficient for domain-sensitive translation scenarios. To address these issues, this paper proposes DKA-LLM-MT, a large-language-model-enhanced adaptive optimization framework for data-limited multi-domain machine translation. The framework follows a data–model–reliability design. First, a domain-knowledge-constrained data augmentation strategy generates pseudo-parallel corpora under terminology, semantic consistency, and domain-style constraints. Second, a retrieval-augmented parameter-efficient adaptation mechanism integrates domain memory retrieval, lightweight LoRA adapters, and dynamic domain routing. Third, a reliability-aware optimization mechanism incorporates semantic fidelity, terminology consistency, and hallucination risk into both training-time data selection and inference-time candidate reranking. Experiments are conducted on five public data-limited domain translation benchmarks covering medical, legal, technical, news, and spoken-style texts. The proposed method achieves an average BLEU of 36.18, chrF of 62.14, COMET of 0.816, and TER of 40.62, consistently outperforming strong neural, multilingual, and LLM-based baselines. Additional matched-backbone and same-language-pair analyses are included to separate the effect of domain adaptation from language-pair variation. Reliability evaluation further shows that DKA-LLM-MT improves terminology accuracy to 89.6% and reduces hallucination rate to 3.2%. The proposed framework provides an effective and reliable solution for domain-sensitive machine translation under limited bilingual supervision and offers practical support for bilingual reading, specialized translation assistance, and domain-oriented language learning.
The research offers a Lotus Effect-Attention-based Bi-directional Gated Recurrent Unit (LE-Att-Bi-GRU) deep learning model for automatic translation quality assessment that improves semantic representation by incorporating a lotus-inspired division method that decreases noise and focuses essential semantic cues.
The findings show that manual transliteration consistently yields the best translation performance, while noisy automatic romanization reduces these gains, and that LLM-based translation can be competitive with, and in some settings outperform, fine-tuned NMT systems, although this advantage comes with lower interpretability.
A. Mansurova, Meruert Bekmukhamedova, Bekarys Baibolat et al.· Electronics· 0 citations
PAMT is proposed, a process-aligned training framework that combines cold-start domain-aware Long-CoT supervision with reinforcement learning that improves over base models, outperforms MT-specialized baselines on average, and remains competitive with strong LLMs/LRMs across in-domain, OOD, and multilingual settings.
Yongshi Ye, Biao Fu, Chongxuan Huang et al.· 0 citations
