Papers by Son Nguyen
VlogQA: Task, Dataset, and Baseline Models for Vietnamese Spoken-Based Machine Reading Comprehension (2024.eacl-long)
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| Challenge: | Existing datasets for machine reading comprehension tasks in Vietnamese focus on written documents, such as Wikipedia articles, online newspapers, or textbooks. |
| Approach: | They propose to capture Vietnamese spoken language in natural settings and use it to create a machine-learning corpus for machine reading comprehension tasks. |
| Outcome: | The proposed corpus consists of 10,076 question-answer pairs based on 1,230 transcript documents sourced from YouTube . |
Enhance Incomplete Utterance Restoration by Joint Learning Token Extraction and Text Generation (2022.naacl-main)
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| Challenge: | omitted tokens from the context contribute to incomplete utterance restoration (IUR) understanding conversational interactions through NLP has become important with increasing connectivity and range of capabilities. |
| Approach: | They propose a model for incomplete utterance restoration called JET . they construct a Picker that identifies omitted tokens and two label creation methods to support the picker. |
| Outcome: | The proposed model is better than pretrained T5 and non-generative language model methods on four benchmark datasets in extraction and abstraction scenarios. |
VLUE: A New Benchmark and Multi-task Knowledge Transfer Learning for Vietnamese Natural Language Understanding (2024.findings-naacl)
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| Challenge: | a lack of standard evaluation metrics and benchmarks makes it difficult to identify strengths of Vietnamese NLP models. |
| Approach: | They propose to establish a standardized set of benchmarks for Vietnamese NLU . they propose to evaluate Vietnamese language understanding models using a pre-trained model . |
| Outcome: | The proposed model combines proficiency of a multilingual pre-trained model with Vietnamese linguistic knowledge. |
Region-Grounded Report Generation for 3D Medical Imaging: A Fine-Grained Dataset and Graph-Enhanced Framework (2026.acl-long)
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Cong Huy Nguyen, Son Dinh Nguyen, Guanlin Li, Tuan Dung Nguyen, Aditya Narayan Sankaran, Mai Huy Thong, Thanh Trung Nguyen, Mai Hong Son, Reza Farahbakhsh, Phi Le Nguyen, Noel Crespi
| Challenge: | Current methods map whole volumes to reports, ignoring the clinical workflow of analyzing localized Regions of Interest (RoIs) Current models exhibit suboptimal accuracy and are prone to significant hallucinations. |
| Approach: | They propose a framework that mimics the professional radiologist diagnostic workflow by employing graph-based relational modules to capture dependencies between RoI attributes. |
| Outcome: | The proposed framework surpasses existing models by 19.7% in BLEU and 4.7% in ROUGE-L while achieving a 45.8% improvement in clinical metrics. |
VMLU Benchmarks: A comprehensive benchmark toolkit for Vietnamese LLMs (2025.acl-long)
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Cuc Thi Bui, Nguyen Truong Son, Truong Van Trang, Lam Viet Phung, Pham Nhut Huy, Hoang Anh Le, Quoc Huu Van, Phong Nguyen-Thuan Do, Van Le Tran Truc, Duc Thanh Chau, Le-Minh Nguyen
| Challenge: | The evolution of Large Language Models (LLMs) has underscored the need for benchmarks designed for various languages and cultural contexts. |
| Approach: | They propose to use Vietnamese multitask language understanding (VMLU) benchmarks to assess different capabilities of LLMs, including general knowledge, reading comprehension, reasoning, and conversational skills. |
| Outcome: | The VMLU Benchmarks assess LLMs' general knowledge, reading comprehension, reasoning, and conversational skills. |
Conversation Understanding using Relational Temporal Graph Neural Networks with Auxiliary Cross-Modality Interaction (2023.emnlp-main)
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| Challenge: | Emotion recognition is a crucial task for human conversation understanding . multimodal data, e.g., language, voice, and facial expressions, add complexity to the task. |
| Approach: | They propose a relational temporal Graph Neural Network with Auxiliary Cross-Modality Interaction framework that captures conversation-level cross-modality interactions and utterance-level temporal dependencies with modality-specific manner for conversation understanding. |
| Outcome: | The proposed framework captures conversation-level cross-modality interactions and utterance-level temporal dependencies with the modality-specific manner for conversation understanding. |