Papers by Nguyen Minh
Structural and Functional Decomposition for Personality Image Captioning in a Communication Game (2020.findings-emnlp)
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| Challenge: | Personality image captioning (PIC) aims to describe an image with a natural language caption given a personality trait. |
| Approach: | They propose to use a communication game between a speaker and a listener to generate captions for PIC. |
| Outcome: | The proposed model achieves state-of-the-art performance for personal image captioning (PIC) the proposed model is based on a communication game between a speaker and a listener . |
XTRA: Cross-Lingual Topic Modeling with Topic and Representation Alignments (2025.findings-emnlp)
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| Challenge: | XTRA aims to uncover shared semantic themes across languages . previous methods have achieved improvements in topic diversity but struggle to ensure high topic coherence and consistent alignment across languages. |
| Approach: | a new framework unifies Bag-of-Words modeling with multilingual embeddings is proposed to address this problem . XTRA introduces two core components: (1) representation alignment and (2) topic alignment to enforce cross-lingual consistency. |
| Outcome: | XTRA outperforms baselines in topic coherence, diversity, and alignment quality on multilingual corpora. |
Multimodal neural pronunciation modeling for spoken languages with logographic origin (D18-1)
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| Challenge: | Graphemes of most languages encode pronunciation, though some are more explicit than others . pronunciation modeling in logographic languages requires decomposing logographs into subunits . |
| Approach: | They propose a multimodal approach to predict pronunciation of Cantonese logographic characters using neural networks. |
| Outcome: | The proposed framework improves performance by 18.1% and 25.0% respectively to unimodal and multimodal baselines. |
FAID: Fine-grained AI-generated Text Detection using Multi-task Auxiliary and Multi-level Contrastive Learning (2026.eacl-long)
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Minh Ngoc Ta, Dong Cao Van, Duc-Anh Hoang, Minh Le-Anh, Truong Nguyen, My Anh Tran Nguyen, Yuxia Wang, Preslav Nakov, Dinh Viet Sang
| Challenge: | Existing binary detection frameworks for human-written, LLM-generated and human-LLM collaborative texts are challenging . a recent study focused on binary detection, i.e., human vs. LLM, or on fine-grained detection limited to English. |
| Approach: | They propose a fine-grained detection framework to classify text into three categories . they use multilingual datasets and a multi-domain, multi-generator dataset . |
| Outcome: | The proposed framework outperforms baselines on unseen domains and new LLMs. |
Event Causality Identification via Generation of Important Context Words (2022.starsem-1)
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| Challenge: | Prior work focused on identifying causal relation between two event mentions . current models do not output important contexts for causal prediction of two mentions. |
| Approach: | They propose to use dependency path generation as a complementary task for ECI. |
| Outcome: | The proposed model can generate both causal relation and dependency path words from input sentences. |
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/. |
VIMQA: A Vietnamese Dataset for Advanced Reasoning and Explainable Multi-hop Question Answering (2022.lrec-1)
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| Challenge: | Existing Vietnamese Question Answering (QA) datasets do not explore the model’s ability to perform advanced reasoning and provide evidence to explain the answer. |
| Approach: | They propose to use Vietnamese as a question-answer dataset with 10,000 Wikipedia-based multi-hop question-and-answ pairs to test model's ability to reason and explain the answer. |
| Outcome: | The proposed dataset is in Vietnamese, a low-resource language. |
Functional Overlap Reranking for Neural Code Generation (2024.findings-acl)
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| Challenge: | Code Large Language Models (CodeLLMs) have ushered in a new era in code generation, but selecting the best code solutions remains a challenge. |
| Approach: | They propose a new reranking strategy that quantifies the functional overlap between solution clusters to provide a better ranking strategy for code solutions. |
| Outcome: | Empirical results show that the proposed method surpasses state-of-the-art methods on the pass@1 score. |
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/. |
VN-MTEB: Vietnamese Massive Text Embedding Benchmark (2026.findings-eacl)
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| Challenge: | a lack of large-scale test datasets makes it difficult to evaluate AI models before deploying them in real-world projects. |
| Approach: | They propose a Vietnamese benchmark for embedding models that leverages large language models and embeddable models to translate and filter samples from the Massive Multilingual Text Embedding Benchmark. |
| Outcome: | The proposed benchmark outperforms existing models in Vietnamese and English tasks with 41 datasets. |
HierarchyNet: Learning to Summarize Source Code with Heterogeneous Representations (2024.findings-eacl)
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| Challenge: | Existing code summarization approaches ignore the interplay of dependencies among program elements and code hierarchy. |
| Approach: | They propose a code summarization approach utilizing Heterogeneous Code Representations (HCRs) and HierarchyNet. |
| Outcome: | The proposed method improves on existing models and pre-trained models. |
ZeLa: Advancing Zero-Shot Multilingual Semantic Parsing with Large Language Models and Chain-of-Thought Strategies (2024.lrec-main)
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| Challenge: | Existing approaches to augment multilingual datasets with labeled English data are lacking in annotated data. |
| Approach: | They propose a framework to augment English data and then use it to train parsers . they propose to use multilingual chain-of-thought prompting techniques to augment other languages' data . |
| Outcome: | The proposed framework augments English data in other languages and trains them with no demonstration samples in target languages. |
StructSP: Efficient Fine-tuning of Task-Oriented Dialog System by Using Structure-aware Boosting and Grammar Constraints (2023.findings-acl)
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| Challenge: | Existing models that learn hierarchical structure information representations do not perform well on task-oriented dialog systems. |
| Approach: | They propose a hierarchical structure information representation model that reinforces the semantic awareness of a pre-trained language model by a two-step fine-tuning mechanism. |
| Outcome: | The proposed model is better than existing models at learning the contextual representations of utterances embedded within its hierarchical semantic structure and improves system performance. |
Graph Convolutional Networks for Event Causality Identification with Rich Document-level Structures (2021.naacl-main)
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| Challenge: | Existing models for document-level Event Causality Identification (ECI) are limited to intra-sentence contexts where event mention pairs are presented in the same sentences. |
| Approach: | They propose a deep learning model that accepts inter-sentence event mention pairs . they use interaction graphs to capture relevant connections between important objects . |
| Outcome: | The proposed model achieves state-of-the-art on two benchmark datasets. |
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. |
Who is Killed by Police: Introducing Supervised Attention for Hierarchical LSTMs (C18-1)
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| Challenge: | Existing methods to identify police killings from text have not been applied to this problem . et al., 2017: finding names of people killed by police is a critical problem despite public attention . |
| Approach: | They propose a method to deal with multiple appearances of police names in documents . they propose hierarchical LSTMs to model multiple sentences that contain names of interests . |
| Outcome: | The proposed method yields state-of-the-art performance for police killing detection . it relies on hierarchical LSTMs to model the multiple sentences that contain the person names of interests . |