Papers by Thong Nguyen
Encoding and Controlling Global Semantics for Long-form Video Question Answering (2024.emnlp-main)
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| Challenge: | Existing methods to find answers for long videos fail to reason over the whole sequence of video, leading to sub-optimal performance. |
| Approach: | They propose a state space layer to integrate global semantics into video . they use a gating unit to enable controllability over the flow of global semantic into visual representations. |
| Outcome: | The proposed framework is able to integrate global semantics into visual representations. |
Improving Multimodal Sentiment Analysis: Supervised Angular margin-based Contrastive Learning for Enhanced Fusion Representation (2023.findings-emnlp)
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| Challenge: | Existing methods for multimodal sentiment analysis focus on forming positive and negative pairs, neglecting the variation in sentiment scores within the same class. |
| Approach: | They propose a framework to enhance discrimination and generalizability of the multimodal representation and overcome biases in the fusion vector’s modality. |
| Outcome: | The proposed model improves discrimination and generalizability of the multimodal representation and overcomes biases in the fusion vector’s modality. |
Gradient-Boosted Decision Tree for Listwise Context Model in Multimodal Review Helpfulness Prediction (2023.findings-acl)
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| Challenge: | Existing studies have shown that FCNNs perform inefficient splitting for review features, making it difficult to clearly differentiate helpful from unhelpful reviews. |
| Approach: | They propose a listwise attention network that captures the MRHP ranking context and a pairwise optimization objective that enhances model generalization. |
| Outcome: | The proposed framework achieves state-of-the-art results and polished generalization performance on two large-scale MRHP benchmark datasets. |
Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires (2022.acl-long)
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| Challenge: | Existing approaches to identify mental health conditions using social media are limited by the presence of symptoms described in a questionnaire used by clinicians. |
| Approach: | They propose to ground a model in PHQ9's symptoms to improve generalization . they also show that this approach can still perform competitively on in-domain data. |
| Outcome: | The proposed approach can perform competitively on in-domain data while improving generalizability and generalisability. |
Modeling Dynamic Topics in Chain-Free Fashion by Evolution-Tracking Contrastive Learning and Unassociated Word Exclusion (2024.findings-acl)
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| Challenge: | Existing dynamic topic models lack the ability to reveal the evolution of topics . Existing models suffer from repetitive topic and unassociated topic issues . |
| Approach: | They propose a new evolution-tracking contrastive learning method that builds the similarity relations among dynamic topics and an unassociated word exclusion method to avoid unassociated topics. |
| Outcome: | The proposed model outperforms state-of-the-art models on downstream tasks and is robust to evolution intensities. |
Continual Safety Alignment via Gradient-Based Sample Selection (2026.findings-acl)
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| Challenge: | Large language models require continuous adaptation to new domains, tasks, and evolving requirements. |
| Approach: | They propose a gradient-based sample selection method that filters high-gradient samples during fine-tuning. |
| Outcome: | The proposed method significantly improves alignment preservation while maintaining competitive task performance on continual domain tasks. |
Adaptive Contrastive Learning on Multimodal Transformer for Review Helpfulness Prediction (2022.emnlp-main)
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| Challenge: | Modern review helpfulness prediction systems focus on polishing cross-modal representations and suffer from inferior optimization. |
| Approach: | They propose a method to polish cross-modal relation representations by learning mutual information through contrastive learning. |
| Outcome: | The proposed framework outperforms baselines and achieves state-of-the-art results on two publicly available datasets. |
Video-Language Understanding: A Survey from Model Architecture, Model Training, and Data Perspectives (2024.findings-acl)
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Thong Nguyen, Yi Bin, Junbin Xiao, Leigang Qu, Yicong Li, Jay Zhangjie Wu, Cong-Duy Nguyen, See-Kiong Ng, Anh Tuan Luu
| Challenge: | Existing video-language understanding systems with human-like senses can mimic both our linguistic medium and visual environment with temporal dynamics. |
| Approach: | They propose to develop video-language understanding systems with human-like senses . they summarize their methods and highlight challenges associated with them . |
| Outcome: | The proposed models perform well in a variety of tasks and domains. |
DyVo: Dynamic Vocabularies for Learned Sparse Retrieval with Entities (2024.emnlp-main)
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| Challenge: | Learned Sparse Retrieval (LSR) models use vocabularies from pre-trained transformers, which often split entities into nonsensical fragments. |
| Approach: | They propose to enhance the LSR vocabulary with Wikipedia concepts and entities, enabling the model to resolve ambiguities more effectively and stay current with evolving knowledge. |
| Outcome: | The proposed model outperforms state-of-the-art models across three entity-rich document ranking datasets. |
Enriching and Controlling Global Semantics for Text Summarization (2021.emnlp-main)
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| Challenge: | Abstractive summarization models have been proven effective in creating fluent and informative summaries, but they suffer from the short-range dependency problem, causing them to produce summary that miss the key points of document. |
| Approach: | They propose a neural topic model empowered with normalizing flow to capture global semantics of the document and integrate them into the summarization model. |
| Outcome: | The proposed model outperforms state-of-the-art summarization models on five common text summarizing datasets, namely CNN/DailyMail, XSum, Reddit TIFU, arXiv, and PubMed. |
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. |
DemaFormer: Damped Exponential Moving Average Transformer with Energy-Based Modeling for Temporal Language Grounding (2023.findings-emnlp)
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| Challenge: | Temporal Language Grounding (TLG) is a task to determine temporal boundaries of video moments that correspond to a language query. |
| Approach: | They propose an energy-based model framework to explicitly learn moment-query distributions. |
| Outcome: | The proposed model outperforms the state-of-the-art models on four public temporal language grounding datasets. |
KDMCSE: Knowledge Distillation Multimodal Sentence Embeddings with Adaptive Angular margin Contrastive Learning (2024.naacl-long)
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| Challenge: | Existing work on multimodal sentence embeddings took negative samples without reviewing, resulting in noisy and noisy negative samples. |
| Approach: | They propose a multimodal contrastive learning approach that inherits the knowledge from the teacher model to learn the difference between positive and negative instances. |
| Outcome: | The proposed approach can detect noisy and wrong negative samples before they are calculated in the contrastive objective. |
SERVAL: Surprisingly Effective Zero-Shot Visual Document Retrieval Powered by Large Vision and Language Models (2025.emnlp-main)
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| Challenge: | Visual Document Retrieval (VDR) relies on text-to-image retrieval using specialized bi-encoders . et al., 2022, 2024, 2021, 2023, 2026, 2030, 2040, 2050, 2060) document retrieval bridges human or artificial agents to the most relevant information, authors say . |
| Approach: | They propose a zero-shot visual document retrieval method that uses bi-encoders . they propose 63.4% nDCG@5 for visual document capture and a reusable semantic proxy . |
| Outcome: | The proposed method surpasses the strongest specialised multi-vector visual document encoder on the ViDoRe-v2 benchmark and scales similarly on MIRACL-VISION with broader multilingual coverage. |