Papers by Tho Quan
When Morphology Hides in Plain Sight: Breaking the Isolation in Vietnamese and Beyond (2026.acl-long)
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| Challenge: | Adaptive Boundary-Token Fusion and a Morpheme-Aware Attention Bias are used to encode monosyllabic morphemes. |
| Approach: | They propose a morpheme-aware Transformer that augments a pretrained Vietnamese encoder with two lightweight inductive biases. |
| Outcome: | The proposed morpheme-aware Transformer outperforms strong baselines on Vietnamese POS, NER, and sentence-level classification benchmarks. |
Crossing Linguistic Horizons: Finetuning and Comprehensive Evaluation of Vietnamese Large Language Models (2024.findings-naacl)
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| Challenge: | Existing open-source LLMs exhibit limited effectiveness in processing Vietnamese . lack of systematic benchmark datasets and metrics tailored for Vietnamese LLM evaluation exacerbates these issues. |
| Approach: | They propose to fine tune LLMs specifically for Vietnamese and develop a framework for evaluation . they find that larger models introduce more biases and uncalibrated outputs . |
| Outcome: | The proposed framework finetunes LLMs specifically for Vietnamese and provides a framework for evaluation . |
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. |
HiGraAgent: Dual-Agent Adaptive Reasoning over Hierarchical Knowledge Graph for Open Domain Multi-hop Question Answering (2026.findings-eacl)
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| Challenge: | Existing approaches to multi-hop question answering lack a robust and flexible approach to QA . prior work showed compositionality gap persists even for Large Language Models . |
| Approach: | They propose a framework that unifies graph-based retrieval with adaptive reasoning . HiGraAgent uses a hierarchical knowledge Graph with entity alignment . |
| Outcome: | The proposed framework outperforms the strongest graph-based method on hotpotQA, 2WikiMultihopQA, and MuSiQue. |