Papers by Nguyen Vo
Improving Vietnamese-English Medical Machine Translation (2024.lrec-main)
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| Challenge: | Existing high-quality Vietnamese-English parallel datasets are inadequate for translation training. |
| Approach: | They introduce a high-quality Vietnamese-English parallel dataset for medical translation . they compare Google Translate, ChatGPT, and pre-trained bilingual/multilingual models . |
| Outcome: | The proposed dataset is compared with translation models from Google Translate and ChatGPT. |
Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detection (2021.eacl-main)
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| Challenge: | Existing methods to fact-check information focus on word-level attention or evidence-level focus, which may result in suboptimal performance. |
| Approach: | They propose a Hierarchical Multi-head Attentive Network to fact-check textual claims using word-level attention and document-level focus. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two real-word datasets. Improvements over baselines are from 6% to 18%. |
OZSpeech: One-step Zero-shot Speech Synthesis with Learned-Prior-Conditioned Flow Matching (2025.acl-long)
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| Challenge: | Text-to-speech systems have seen significant advances in recent years, driven by improvements in deep learning and neural network architectures. |
| Approach: | They propose a method to explore optimal transport conditional flow matching with one-step sampling and a learned prior as the condition, effectively disregarding preceding states and reducing the number of sampling steps. |
| Outcome: | The proposed method achieves promising performance over existing methods in content accuracy, naturalness, prosody generation, and speaker style preservation. |
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. |
Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News (2020.emnlp-main)
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| Challenge: | fabricated stories and hoaxes are still pervading our cyberspace. |
| Approach: | They propose a framework to search for fact-checking articles that address the content of an original tweet that may contain misinformation posted by online users. |
| Outcome: | The proposed framework can detect and disseminate fake news on real-world datasets and warn fake news posters and online users about misinformation. |
Enhancing Incremental Summarization with Structured Representations (2024.findings-emnlp)
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| Challenge: | Large language models struggle with processing extensive input contexts, leading to redundancy or incoherency. |
| Approach: | They propose a chain-of-key update based on JSON structured memory representations to improve summarization performance by 40% and 14% on two public datasets. |
| Outcome: | The proposed method improves summarization performance by 40% and 14% on two datasets. |