Papers by Nguyen Thi Ngoc Diep
Mutual-pairing Data Augmentation for Fewshot Continual Relation Extraction (2025.naacl-long)
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Nguyen Hoang Anh, Quyen Tran, Thanh Xuan Nguyen, Nguyen Thi Ngoc Diep, Linh Ngo Van, Thien Huu Nguyen, Trung Le
| Challenge: | Existing methods for Few-shot Continual Relation Extraction struggle with catastrophic forgetting and overfitting. |
| Approach: | They propose a method that transforms single input sentences into complex texts by integrating old and new data. |
| Outcome: | The proposed method sharpens model focus and improves model performance . it also uncovers fascinating behaviors of Sharpness-Aware Minimization (SAM) in Few-shot Continual Learning. |
LLM-XTM: Enhancing Cross-Lingual Topic Models with Large Language Models (2026.acl-long)
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| Challenge: | Existing cross-lingual topic models depend on sparse bilingual resources and often yield incoherent or weakly aligned topics. |
| Approach: | They propose a framework that integrates LLM-guided topic refinement with self-consistency uncertainty quantification to enable black-box, stable, and scalable enhancement of cross-lingual topic models. |
| Outcome: | Experiments on multilingual corpora show that the proposed framework achieves superior topic coherence and alignment while reducing reliance on bilingual dictionaries and expensive LLM calls. |
EMO: Embedding Model Distillation via Intra-Model Relation and Optimal Transport Alignments (2025.emnlp-main)
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| Challenge: | Existing methods for knowledge distillation focus on direct output alignment, neglecting this crucial structural information. |
| Approach: | They propose a framework for knowledge distillation that maps tokens one-to-one and aligns attention matrix patterns using Centered Kernel Alignment. |
| Outcome: | The proposed framework significantly outperforms existing CTKD baselines. |
SRA: Span Representation Alignment for Large Language Model Distillation (2026.acl-long)
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Quoc Phong Dao, Hoang Son Nguyen, Pham Khanh Chi, Tung Nguyen, Linh Ngo Van, Nguyen Thi Ngoc Diep, Trung Le
| Challenge: | Existing approaches to cross-tokenizer knowledge distillation are brittle and sensitive to discrepancies between tokenizers. |
| Approach: | They propose a framework that shifts the unit of alignment from tokens to robust, tokenizer-agnostic spans and employ a geometric regularizer to preserve the structural integrity of the representation space. |
| Outcome: | The proposed framework outperforms state-of-the-art methods in cross-architecture distillation experiments. |
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. |
MemORAI: Memory Organization and Retrieval via Adaptive Graph Intelligence for LLM Conversational Agents (2026.findings-acl)
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Hung Pham Van, Nguyen Manh Hieu, Khang Pham Tran Tuan, Nam Le Hai, Linh Ngo Van, Nguyen Thi Ngoc Diep, Trung Le
| Challenge: | Existing graph-based memory systems suffer from information dilution, absent provenance tracking, and uniform retrieval that ignores query context. |
| Approach: | They propose a framework that integrates memory organization and retrieval via a Graph Intelligence framework. |
| Outcome: | Evaluated on LOCOMO and LongMemEval benchmarks, MemORAI achieves state-of-the-art performance in memory retrieval and personalized response generation. |
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. |
ToVo: Toxicity Taxonomy via Voting (2025.findings-naacl)
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Tinh Son Luong, Thanh-Thien Le, Thang Viet Doan, Linh Ngo Van, Thien Huu Nguyen, Nguyen Thi Ngoc Diep
| Challenge: | Existing toxic content detection models face limitations due to the closed-source nature of training data and the paucity of explanations for their evaluation mechanism. |
| Approach: | They propose a mechanism that integrates voting and chain-of-thought processes to produce a high-quality open-source dataset for toxic content detection. |
| Outcome: | The proposed model improves transparency and customizability while facilitating better fine-tuning for specific use cases. |
TALAS: Teacher-Anchored Layer Alignment with Adaptive Sharpness-Aware Minimization for Embedding Distillation (2026.acl-long)
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Quoc Phong Dao, Hoang Son Nguyen, Pham Khanh Chi, Linh Ngo Van, Nguyen Thi Ngoc Diep, Thien Huu Nguyen, Trung Le
| Challenge: | Existing methods that require a student to strictly mimic the teacher’s sentence embeddings or internal features often incur prohibitive computational costs and yield suboptimal performance due to the inherent capacity gap. |
| Approach: | They propose a Teacher-Anchored mechanism that selectively distills final sentence embeddings only into the student’s upper layers, thereby reducing overhead while respecting capacity constraints. |
| Outcome: | Empirical results show that TALAS outperforms existing methods while maintaining high performance. |
MaGiX: A Multi-Granular Adaptive Graph Intelligence Framework for Enhancing Cross-Lingual RAG (2025.findings-emnlp)
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Nguyen Manh Hieu, Vu Lam Anh, Hung Pham Van, Nam Le Hai, Linh Ngo Van, Nguyen Thi Ngoc Diep, Thien Huu Nguyen
| Challenge: | Recent advances in Graph-based RAG (GRAG) frameworks focus on knowledge graphs for cross-lingual retrieval. |
| Approach: | They propose a new GRAG framework for cross-lingual question answering . MaGiX constructs a multi-granular cross-linguistic knowledge graph using fine-grained attribute descriptions and cross-synonym edges. |
| Outcome: | The proposed framework outperforms prior GRAG systems in retrieval accuracy and generation quality. |