Papers by Tuan Le
FoTo: Targeted Visual Topic Modeling for Focused Analysis of Short Texts (2024.lrec-main)
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| Challenge: | Existing topic models perform a full corpus analysis that treats all topics equally, making them not on target. |
| Approach: | They propose a targeted topic model for semantic short-text embedding which aims to learn all topics and low-dimensional visual representations of documents while preserving relevant topics. |
| Outcome: | The proposed model learns all topics and low-dimensional visual representations while preserving relevant topics in the visualization space. |
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. |
AutoNLU: An On-demand Cloud-based Natural Language Understanding System for Enterprises (2020.aacl-demo)
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| Challenge: | AutoNLU is an on-demand cloud-based system that enables users to create and edit datasets and train and test different state-of-the-art NLU models. |
| Approach: | They introduce an on-demand cloud-based system that provides an easy-to-use interface . they build powerful keyphrase extraction models that achieve state-of-the-art results . |
| Outcome: | The proposed model achieves state-of-the-art on two public benchmarks and is easy to use and use. |
Auto-Encoding Variational Bayes for Inferring Topics and Visualization (2020.coling-main)
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| Challenge: | Visualization and topic modeling are widely used for text analysis, but there is a lack of semantic interpretation as they do not extract topics. |
| Approach: | They propose a fast Auto-Encoding Variational Bayes based inference method for jointly inferring topics and visualization using a single generative model. |
| Outcome: | The proposed method can handle model changes efficiently with little rederivation effort. |
Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering (2020.coling-main)
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| Challenge: | Interpretability and explainability of deep neural net models are always challenging due to their size and complexity. |
| Approach: | They propose to design an explainable, evidence-based memory network architecture that connects current sample with seen samples and bases its decision on these samples. |
| Outcome: | The proposed model can trace errors to training instances that might have caused errors . the proposed model achieves state-of-the-art performance on two popular datasets . |
Conversation Understanding using Relational Temporal Graph Neural Networks with Auxiliary Cross-Modality Interaction (2023.emnlp-main)
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| Challenge: | Emotion recognition is a crucial task for human conversation understanding . multimodal data, e.g., language, voice, and facial expressions, add complexity to the task. |
| Approach: | They propose a relational temporal Graph Neural Network with Auxiliary Cross-Modality Interaction framework that captures conversation-level cross-modality interactions and utterance-level temporal dependencies with modality-specific manner for conversation understanding. |
| Outcome: | The proposed framework captures conversation-level cross-modality interactions and utterance-level temporal dependencies with the modality-specific manner for conversation understanding. |