Papers by Vu Tran
MIPIC: Matryoshka Representation Learning via Self-Distilled Intra-Relational and Progressive Information Chaining (2026.findings-acl)
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Phung Gia Huy, Hai An Vu, Minh-Phuc Truong, Thang Duc Tran, Linh Ngo Van, Thanh Hong Nguyen, Trung Le
| Challenge: | Existing approaches to train dense representations require explicit coordination of how information is arranged across embedding dimensionality and model depth. |
| Approach: | They propose a framework that trains Matryoshka representations using self-distilled intra-relational alignment and Progressive information chaining. |
| Outcome: | The proposed framework produces coherent and compact Matryoshka representations with significant performance advantages under low-dimensional models. |
CovRelex-SE: Adding Semantic Information for Relation Search via Sequence Embedding (2023.eacl-demo)
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| Challenge: | COVID-19 has affected all aspects of human life, causing problems related to acronyms, synonyms, and rare keywords. |
| Approach: | They propose a hybrid relation retrieval system based on embeddings to provide high-quality search results. |
| Outcome: | The proposed system can be accessed through the following URL: http://www.jaist.ac.jp/is/labs/nguyen-lab/systems/covrelex-se/. |
ViGPTQA - State-of-the-Art LLMs for Vietnamese Question Answering: System Overview, Core Models Training, and Evaluations (2023.emnlp-industry)
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| Challenge: | Large language models (LLMs) and their applications in low-resource languages are limited due to lack of training data and benchmarking datasets. |
| Approach: | They propose a question-response system for Vietnamese that uses LLMs . they propose to open-source the model and train it on benchmark datasets based on Vietnamese data . |
| Outcome: | The proposed question answering system for Vietnamese is open-source and performant . it can learn and capture human-like text, but there is a gap in evaluations for Vietnamese . |
Disentangling Language Understanding and Reasoning Structures in Cross-lingual Chain-of-Thought Prompting (2025.findings-emnlp)
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| Challenge: | a recent study has shown that cross-lingual chain-of-thought prompting improves learning in low-resource languages. |
| Approach: | They examine whether benefits of cross-lingual prompting arise from language-specific reasoning structures . authors employ neuron intervention and perturbation techniques to analyze and deactivate language-related reasoning neurons . |
| Outcome: | The proposed study shows that language-specific reasoning structures are essential for reasoning in each language, but have minimal effect on reasoning in other languages. |
CovRelex: A COVID-19 Retrieval System with Relation Extraction (2021.eacl-demos)
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| Challenge: | Existing challenges to making the system more practical include dealing with newly created and unknown data, and solving the performance gap when utilizing present data. |
| Approach: | They propose a scientific paper retrieval system targeting entities and relations via relation extraction on COVID-19 scientific papers. |
| Outcome: | The proposed system can be accessed via https://www.jaist.ac.jp/is/labs/nguyen-lab/systems/covrelex/. |
Introducing a Large-Scale Dataset for Vietnamese POS Tagging on Conversational Texts (2020.lrec-1)
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| Challenge: | POS taggers are trained on informal texts which contain many informal inputs such as acronyms, abbreviations, out-of-vocabulary words, etc. |
| Approach: | They propose a large-scale human-labeled dataset for the Vietnamese POS tagging task on conversational texts and develop an annotation guideline to manually annotate 16.310K sentences using this guideline. |
| Outcome: | The proposed tagging scheme achieved 93.36% accuracy score and higher than the model with handcrafted features and fine-tuning BERT. |
Answering Legal Questions by Learning Neural Attentive Text Representation (2020.coling-main)
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| Challenge: | Existing methods for retrieval-based question answering are limited by legal documents and long and complicated documents. |
| Approach: | They propose a retrieval-based model for answering legal questions at the article level by learning neural attentive text representation. |
| Outcome: | The proposed model outperforms state-of-the-art retrieval-based methods on an annotated corpus of 5,922 Vietnamese legal questions in terms of recall and NDCG. |
ViHealthBERT: Pre-trained Language Models for Vietnamese in Health Text Mining (2022.lrec-1)
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| Challenge: | Recent large-scale language models show remarkable achievements in key NLP tasks such as Question Answering and Text Summarization. |
| Approach: | They propose a domain-specific pre-trained Vietnamese language model that outperforms the general domain language models. |
| Outcome: | The proposed model outperforms the general domain language models in Vietnamese datasets while outperforming the general-domain language models. |
ViDeBERTa: A powerful pre-trained language model for Vietnamese (2023.findings-eacl)
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| Challenge: | Existing models for Vietnamese that perform well on downstream tasks, such as Question answering, are based on Transformer. |
| Approach: | They propose a pre-trained monolingual Vietnamese model with three versions . they fine-tune and evaluate the model on three important natural language downstream tasks, Part-of-speech tagging, Named-entity recognition, and Question answering. |
| Outcome: | The proposed model outperforms the existing model on three important natural language downstream tasks, Part-of-speech tagging, Named-entity recognition, and Question answering. |