Nguyen Van-Vinh

Also published as: Nguyen Van Vinh


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A Fast Method to Filter Noisy Parallel Data WMT2023 Shared Task on Parallel Data Curation
Nguyen-Hoang Minh-Cong | Nguyen Van Vinh | Nguyen Le-Minh
Proceedings of the Eighth Conference on Machine Translation

The effectiveness of a machine translation (MT) system is intricately linked to the quality of its training dataset. In an era where websites offer an extensive repository of translations such as movie subtitles, stories, and TED Talks, the fundamental challenge resides in pinpointing the sentence pairs or documents that represent accurate translations of each other. This paper presents the results of our submission to the shared task WMT2023 (Sloto et al., 2023), which aimed to evaluate parallel data curation methods for improving the MT system. The task involved alignment and filtering data to create high-quality parallel corpora for training and evaluating the MT models. Our approach leveraged a combination of dictionary and rule-based methods to ensure data quality and consistency. We achieved an improvement with the highest 1.6 BLEU score compared to the baseline system. Significantly, our approach showed consistent improvements across all test sets, suggesting its efficiency.


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The UET-ICTU Submissions to the VLSP 2020 News Translation Task
Ngo Thi-Vinh | Nguyen Minh-Thuan | Nguyen Hoang Minh Cong | Nguyen Hoang-Quan | Nguyen Phuong-Thai | Nguyen Van-Vinh
Proceedings of the 7th International Workshop on Vietnamese Language and Speech Processing


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The English-Vietnamese machine translation system for IWSLT 2015
Viet Hong Tran | Huyen Vu Thong | Nguyen Van-Vinh | Trung Le Tien
Proceedings of the 12th International Workshop on Spoken Language Translation: Evaluation Campaign