Vietnam’s traditional medical texts were historically written in Classical Chinese using Sino-Vietnamese pronunciations. As the Vietnamese language transitioned to a Latin-based national script and interest in integrating traditional medicine with modern healthcare grows, accurate translation of these texts has become increasingly important. However, the diversity of terminology and the complexity of translating medical entities into modern contexts pose significant challenges. To address this, we propose a method that fine-tunes large language models (LLMs) using augmented data and a Hybrid Entity Masking and Replacement (HEMR) strategy to improve named entity translation. We also introduce a parallel named entity translation dataset specifically curated for traditional Vietnamese medicine. Our evaluation across multiple LLMs shows that the proposed approach achieves a translation accuracy of 71.91%, demonstrating its effectiveness. These results underscore the importance of incorporating named entity awareness into translation systems, particularly in low-resource and domain-specific settings like traditional Vietnamese medicine.

This paper describes JHARNA-MT, our system for the MMLoSo 2025 Shared Task on translation between high-resource languages (Hindi, English) and four low-resource Indic tribal languages: Bhili, Gondi, Mundari, and Santali. The task poses significant challenges, including data sparsity, morphological richness, and structural divergence across language pairs. To address these, we propose a hybrid translation pipeline that integrates non-parametric retrieval, lexical statistical machine translation (SMT), and LoRA-tuned NLLB-200 neural machine translation under a unified Minimum Bayes Risk (MBR) decoding framework. Exact and fuzzy retrieval exploit redundancy in government and administrative texts, SMT with diagonal alignment priors and back-translation provides lexically faithful hypotheses, and the NLLB-LoRA component contributes fluent neural candidates. MBR decoding selects consensus translations using a metric-matched utility based on a weighted combination of BLEU and chrF, mitigating the complementary error modes of SMT and NMT. Our final system, further enhanced with script-aware digit normalization and entity-preserving post-processing, achieves a private leaderboard score of 186.37 and ranks 2nd overall in the shared task, with ablation studies confirming the contribution of each component.

Recent space missions such as Hubble, Chandra, and JWST have produced a rapidly growing body of scientific literature. Maintaining telescope bibliographies is essential for mission assessment and research traceability, yet current curation processes rely heavily on manual annotation and do not scale. To facilitate progress in this direction, the TRACS @ WASP 2025 shared task provides a benchmark for automatic telescope bibliographic classification based on scientific publications. In this work, we conduct a comparative study of modeling strategies for this task. We first explore traditional machine learning methods such as multinomial Naive Bayes with TF–IDF and CountVectorizer representations. We then evaluate transformer-based multi-label classification using BERT-based scientific language models. Finally, we investigate a task-wise classification approach, where we decompose the problem into separate prediction tasks and train a dedicated model for each. In addition, we experiment with a limited-resource LLM-based approach, showing that even without full fine-tuning and using only a partial subset of the training data, LLMs exhibit promising potential for telescope classification. Our best system achieves a macro F1 of 0.72 with BERT-based models on the test evaluation, substantially outperforming the official openai-gpt-oss-20b baseline (0.31 macro F1).

Question answering involves creating answers to questions. With the growth of large language models, the ability of question-answering systems has dramatically improved. However, there is a lack of Vietnamese abstractive question-answering datasets, especially in the medical domain. Therefore, this research aims to mitigate this gap by introducing ViMedAQA. This **Vi**etnamese **Med**ical **A**bstractive **Q**uestion-**A**nswering dataset covers four topics in the Vietnamese medical domain, including body parts, disease, drugs and medicine. Additionally, the empirical results on the proposed dataset examine the capability of the large language models in the Vietnamese medical domain, including reasoning, memorizing and awareness of essential information.

As the number of language models has increased, various benchmarks have been suggested to assess the proficiency of the models in natural language understanding. However, there is a lack of such a benchmark in Vietnamese due to the difficulty in accessing natural language processing datasets or the scarcity of task-specific datasets. **ViGLUE**, the proposed dataset collection, is a **Vi**etnamese **G**eneral **L**anguage **U**nderstanding **E**valuation benchmark developed using three methods: translating an existing benchmark, generating new corpora, and collecting available datasets. ViGLUE contains twelve tasks and encompasses over ten areas and subjects, enabling it to evaluate models comprehensively over a broad spectrum of aspects. Baseline models utilizing multilingual language models are also provided for all tasks in the proposed benchmarks. In addition, the study of the available Vietnamese large language models is conducted to explore the language models’ ability in the few-shot learning framework, leading to the exploration of the relationship between specific tasks and the number of shots.

Neural Machine Translation (NMT) aims to translate the source- to the target-language while preserving the original meaning. Linguistic information such as morphology, syntactic, and semantics shall be grasped in token embeddings to produce a high-quality translation. Recent works have leveraged the powerful Graph Neural Networks (GNNs) to encode such language knowledge into token embeddings. Specifically, they use a trained parser to construct semantic graphs given sentences and then apply GNNs. However, most semantic graphs are tree-shaped and too sparse for GNNs which cause the over-smoothing problem. To alleviate this problem, we propose a novel Multi-level Community-awareness Graph Neural Network (MC-GNN) layer to jointly model local and global relationships between words and their linguistic roles in multiple communities. Intuitively, the MC-GNN layer substitutes a self-attention layer at the encoder side of a transformer-based machine translation model. Extensive experiments on four language-pair datasets with common evaluation metrics show the remarkable improvements of our method while reducing the time complexity in very long sentences.

Word Order transfer is a compulsory stage and has a great effect on the translation result of a transfer-based machine translation system. To solve this problem, we can use fixed rules (rule-based) or stochastic methods (corpus-based) which extract word order transfer rules between two languages. However, each approach has its own advantages and disadvantages. In this paper, we present a hybrid approach based on fixed rules and Transformation-Based Learning (or TBL) method. Our purpose is to transfer automatically the English word orders into the Vietnamese ones. The learning process will be trained on the annotated bilingual corpus (named EVC: English-Vietnamese Corpus) that has been automatically word-aligned, phrase-aligned and POS-tagged. This transfer result is being used for the transfer module in the English-Vietnamese transfer-based machine translation system.

Machine Translation (MT) is the most interesting and difficult task which has been posed since the beginning of computer history. The highest difficulty which computers had to face with, is the built-in ambiguity of Natural Languages. Formerly, a lot of human-devised rules have been used to disambiguate those ambiguities. Building such a complete rule-set is time-consuming and labor-intensive task whilst it doesn’t cover all the cases. Besides, when the scale of system increases, it is very difficult to control that rule-set. In this paper, we present a new model of learning-based MT (entitled BTL: Bitext-Transfer Learning) that learns from bilingual corpus to extract disambiguating rules. This model has been experimented in English-to-Vietnamese MT system (EVT) and it gave encouraging results.