Papers by Anh Tuan 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. |
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. |
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. |
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. |
KC4MT: A High-Quality Corpus for Multilingual Machine Translation (2022.lrec-1)
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Vinh Van Nguyen, Ha Nguyen, Huong Thanh Le, Thai Phuong Nguyen, Tan Van Bui, Luan Nghia Pham, Anh Tuan Phan, Cong Hoang-Minh Nguyen, Viet Hong Tran, Anh Huu Tran
| Challenge: | In machine translation, Vietnamese is a low-resource language, and the quality of the training corpus is very low. |
| Approach: | They propose a method for building high-quality multilingual parallel corpus in news domain . they also publicize a corpus that includes 500.000 Vietnamese-Chinese bilingual sentence pairs . |
| Outcome: | The proposed method improves the quality of multilingual machine translation in Vietnamese, Laos, and Khmer . the public version includes 500.000 Vietnamese-Chinese bilingual sentence pairs . |
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. |
Fast Word Predictor for On-Device Application (2020.coling-demos)
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| Challenge: | a proposed word prediction model is developed for a chat application serving more than 100 million users. |
| Approach: | They propose a fast word predictor that reduces memory size and inference time on mobile devices. |
| Outcome: | The proposed model reduces memory size and inference time on a mobile device compared with a standard neural network . it achieves robust performance by learning on large text corpora and is available on microsoft's chat app . |
Learning Uncertainty from Sequential Internal Dispersion in Large Language Models (2026.acl-long)
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| Challenge: | Recent approaches to detect hallucinations depend on model internal states to estimate uncertainty, but they focus on last or mean tokens. |
| Approach: | They propose a supervised hallucination detection framework that leverages token-wise, layer-wise features derived from hidden states. |
| Outcome: | The proposed framework outperforms baseline models and avoids large training sets. |
Enhancing Multimodal Entity Linking with Jaccard Distance-based Conditional Contrastive Learning and Contextual Visual Augmentation (2025.naacl-long)
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Cong-Duy T Nguyen, Xiaobao Wu, Thong Thanh Nguyen, Shuai Zhao, Khoi M. Le, Nguyen Viet Anh, Feng Yichao, Anh Tuan Luu
| Challenge: | Existing approaches to multimodal entity linking use contrastive learning to align input sentences and entities, but are limited by their random negative sampling. |
| Approach: | They propose a method to match negative samples with similar attributes using JD-CCL . they also propose 'contextual visual-aid controllable patch transform' experimental results demonstrate the strong effectiveness of their method . |
| Outcome: | The proposed method is able to match negative samples with similar attributes on a multimodal knowledge graph. |
Unlearning Backdoor Attacks for LLMs with Weak-to-Strong Knowledge Distillation (2025.findings-acl)
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| Challenge: | Parameter-efficient fine-tuning (PEFT) can bridge the gap between large language models and downstream tasks, but is vulnerable to malicious attacks. |
| Approach: | They propose a weak-to-strong unlearning algorithm based on feature alignment knowledge distillation to defend against backdoor attacks . they first train a small-scale language model through full-parameter fine-tuning to serve as the clean teacher model and then guide the large-scale poisoned student model in unlearning the backdoor. |
| Outcome: | The proposed method can unlearn backdoor features without compromising model performance. |
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. |
Towards Fast and Accurate Modeling for Cross-Lingual Label Projection (2026.acl-long)
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| Challenge: | Existing methods for label projection are inaccurate or slow for large-scale use. |
| Approach: | They propose to synthesize alignment sequence pairs and fine-tune an encoder model with span alignment objective while controlling data influence during training. |
| Outcome: | The proposed method outperforms state-of-the-art methods while maintaining fast inference speed across 50+ languages. |
ClozeMath: Improving Mathematical Reasoning in Language Models by Learning to Fill Equations (2025.findings-acl)
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| Challenge: | Existing methods to train large language models do not capture how humans learn to think. |
| Approach: | They propose a method to fine-tune large language models for mathematical reasoning by using a text-infilling task that predicts masked equations from a given solution. |
| Outcome: | Experiments on GSM8K, MATH, and GSM-Symbolic show that ClozeMath surpasses baseline Masked Thought in performance and robustness with two test-time scaling decoding algorithms, Beam Search and Chain-of-Thought decoding. |
Who’s Who: Large Language Models Meet Knowledge Conflicts in Practice (2024.findings-emnlp)
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| Challenge: | Recent large-scale pretrained language models excel in tasks requiring natural language understanding, but they often "hallucinate" plausible but incorrect content due to outdated or incorrect pretraining information. |
| Approach: | They propose a public benchmark dataset to examine model’s behavior in knowledge conflict situations. |
| Outcome: | The proposed model induces conflicts by asking about a common property among entities having the same name, resulting in questions with up to 8 distinctive answers. |
Massively Multilingual Instruction-Following Information Extraction (2025.findings-acl)
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| Challenge: | Past literature on information extraction (IE) has focused on a few high-resource languages, hindering their applications on multilingual corpora. |
| Approach: | They propose a collection of data that unifies and standardizes instruction-following multilingual IE and introduce a structure-aware metric that captures partially matched spans. |
| Outcome: | The proposed framework standardizes and unifies 215 manually annotated datasets, covering 96 typologically diverse languages from 18 language families. |
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. |