Papers by Nguyen Dinh
Direct Evaluation of Chain-of-Thought in Multi-hop Reasoning with Knowledge Graphs (2024.findings-acl)
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| Challenge: | Prior research on evaluating large language models focused on answer accuracy, neglecting the correctness of the generated CoT. |
| Approach: | They propose a discriminative and generative CoT evaluation paradigm to assess LLMs’ knowledge of reasoning and the accuracy of the generated CoT. |
| Outcome: | The proposed evaluation paradigm assesses LLMs’ knowledge of reasoning and the accuracy of the generated CoT. |
A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization (N19-1)
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| Challenge: | Existing knowledge graphs with billions of triples are incomplete, i.e., missing a lot of valid triples. |
| Approach: | They propose to embed relationship triples into a capsule network using a convolution layer and multiple filters to generate feature maps. |
| Outcome: | The proposed model outperforms strong search personalization baselines on two benchmark datasets and outperformed previous state-of-the-art models on WN18RR and FB15k-237. |
HiCOT: Improving Neural Topic Models via Optimal Transport and Contrastive Learning (2025.findings-acl)
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| Challenge: | Recent advances in neural topic models (NTMs) have improved topic quality but still face challenges: weak document-topic alignment, high inference costs due to large pretrained language models, and limited modeling of hierarchical topic structures. |
| Approach: | They propose a framework that integrates hierarchical clustering and contrastive learning to refine document-topic relationships using compact PLM-based embeddings. |
| Outcome: | The proposed framework improves topic coherence, topic performance, representation quality and computational efficiency over existing NTMs. |
Improving Vietnamese-English Cross-Lingual Retrieval for Legal and General Domains (2025.naacl-short)
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Toan Ngoc Nguyen, Nam Le Hai, Nguyen Doan Hieu, Dai An Nguyen, Linh Ngo Van, Thien Huu Nguyen, Sang Dinh
| Challenge: | Existing document retrieval systems focus on a single language, targeting resource-rich languages like English or Chinese. |
| Approach: | They propose auxiliary loss function and symmetrical training strategy for cross-lingual retrieval between Vietnamese and English . they propose a dataset that covers the general domain and extends to the legal field . |
| Outcome: | The proposed dataset significantly improves state-of-the-art models on cross-lingual retrieval tasks. |
What Makes a Good Natural Language Prompt? (2025.acl-long)
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| Challenge: | Existing studies on prompt quality show imbalanced support across models and tasks, and research gaps. |
| Approach: | They propose a property- and human-centric framework for evaluating prompt quality . they propose comparing prompt quality to other factors such as adverbs and apverbs . |
| Outcome: | The proposed framework reveals imbalanced support across models and tasks and substantial research gaps. |
Enhancing Discriminative Representation in Similar Relation Clusters for Few-Shot Continual Relation Extraction (2025.naacl-long)
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Anh Duc Le, Nam Le Hai, Thanh Xuan Nguyen, Linh Ngo Van, Nguyen Thi Ngoc Diep, Sang Dinh, Thien Huu Nguyen
| Challenge: | Existing methods for relation extraction (RE) fail to address the problem of similar relations, which contributes to catastrophic forgetting. |
| Approach: | They propose a relation extraction method that utilizes relation descriptions and dynamic clustering to identify similar relations. |
| Outcome: | The proposed method mitigates catastrophic forgetting and outperforms state-of-the-art methods by a large margin. |
GloCOM: A Short Text Neural Topic Model via Global Clustering Context (2025.naacl-long)
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| Challenge: | Existing neural topic models often overlook uncovering hidden topics from short texts due to data sparsity, poor aggregation quality, and difficulty in inferring topic proportions for individual documents. |
| Approach: | They propose a model which constructs global clustering contexts for short texts using text embeddings from pre-trained language models. |
| Outcome: | The proposed model outperforms state-of-the-art models on short texts in topic quality and document representation. |
End-to-End Evaluation for Low-Latency Simultaneous Speech Translation (2023.emnlp-demo)
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Christian Huber, Tu Anh Dinh, Carlos Mullov, Ngoc-Quan Pham, Thai Binh Nguyen, Fabian Retkowski, Stefan Constantin, Enes Ugan, Danni Liu, Zhaolin Li, Sai Koneru, Jan Niehues, Alexander Waibel
| Challenge: | a framework to evaluate low-latency speech translations is currently only limited to specific aspects and is not able to compare different approaches. |
| Approach: | They propose a framework to perform and evaluate low-latency speech translation in realistic conditions. |
| Outcome: | The proposed framework evaluates various aspects of low-latency speech translation under realistic conditions. |
Multi-level Community-awareness Graph Neural Networks for Neural Machine Translation (2022.coling-1)
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| Challenge: | Recent studies have used Graph Neural Networks (GNNs) to encode language knowledge into token embeddings. |
| Approach: | They propose a multi-level community-awareness Graph Neural Network layer to jointly model local and global relationships between words and their linguistic roles in multiple communities. |
| Outcome: | The proposed method reduces time complexity in very long sentences while preserving the original meaning. |
Sharpness-Aware Minimization for Topic Models with High-Quality Document Representations (2025.naacl-long)
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Tung Nguyen, Tue Le, Hoang Tran Vuong, Quang Duc Nguyen, Duc Anh Nguyen, Linh Ngo Van, Sang Dinh, Thien Huu Nguyen
| Challenge: | Recent advances in topic models have significantly enhanced the performance compared to conventional probabilistic approaches. |
| Approach: | They propose to use a topic model optimization method to improve generalization and performance. |
| Outcome: | The proposed method improves topic models on popular benchmark datasets. |
Topic Modeling for Short Texts via Optimal Transport-Based Clustering (2025.findings-acl)
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| Challenge: | Existing approaches to topic modeling are based on probabilistic graphical models or non-negative matrix factorization techniques. |
| Approach: | They propose a method that aligns global clusters with topics to discover topics and learn document representations in topic space. |
| Outcome: | The proposed method outperforms state-of-the-art techniques in short-text topic modeling across commonly used metrics. |
Mitigating Non-Representative Prototypes and Representation Bias in Few-Shot Continual Relation Extraction (2025.acl-long)
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| Challenge: | Existing methods for few-shot continual relation extraction (FCRE) face two main challenges: non-representative prototypes and representation bias. |
| Approach: | They propose to use General Orthogonal Frame to create robust class prototypes . they also utilize label description representations as global class representatives . |
| Outcome: | The proposed method outperforms state-of-the-art methods on well-known benchmarks on well known FCRE benchmarks. |
A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network (N18-2)
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| Challenge: | Existing knowledge base embedding models are incomplete, i.e., missing a lot of valid triples. |
| Approach: | They propose a convolutional neural network embedding model for knowledge base completion that captures global relationships and transitional characteristics. |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets. |
ViGLUE: A Vietnamese General Language Understanding Benchmark and Analysis of Vietnamese Language Models (2024.findings-naacl)
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| Challenge: | Existing benchmarks for natural language understanding have been suggested, but there is a lack of such a benchmark in Vietnamese due to the difficulty in accessing datasets or the scarcity of task-specific datasets. |
| Approach: | They propose to use a benchmark to evaluate Vietnamese language models in a variety of tasks and areas to explore the relationship between specific tasks and the number of shots. |
| Outcome: | The proposed benchmark contains twelve tasks and encompasses over ten areas and subjects, enabling it to evaluate models comprehensively over a broad spectrum of aspects. |
Multi-Dialect Vietnamese: Task, Dataset, Baseline Models and Challenges (2024.emnlp-main)
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| Challenge: | Vietnamese is a low-resource language, but each province has its own distinct pronunciation variations. |
| Approach: | They propose a dataset that captures the rich diversity of 63 provincial dialects spoken in Vietnam. |
| Outcome: | The proposed dataset captures the rich diversity of 63 provincial dialects spoken across Vietnam. |
A Relational Memory-based Embedding Model for Triple Classification and Search Personalization (2020.acl-main)
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| Challenge: | Existing knowledge graph embedding methods do not allow for the prediction of new triples, such as for search personalization tasks. |
| Approach: | They propose a relational memory network to encode potential dependencies in relationship triples by a transformer self-attention mechanism. |
| Outcome: | The proposed model obtains state-of-the-art results on SEARCH17, WN11 and FB13 for the search personalization task, and on a convolutional neural network-based decoder. |
ViMedAQA: A Vietnamese Medical Abstractive Question-Answering Dataset and Findings of Large Language Model (2024.acl-srw)
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| Challenge: | Existing abstractive question-answering datasets in Vietnamese are lacking . |
| Approach: | They propose to introduce a Vietnamese abstractive question-answering corpus to address this gap . they propose to use Vietnamese abstractives to generate answers to questions . |
| Outcome: | The proposed dataset examines the capability of large language models in the Vietnamese medical domain, including reasoning, memorizing and awareness of essential information. |