Duc-Vu Nguyen


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ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing
Nam Nguyen | Thang Phan | Duc-Vu Nguyen | Kiet Nguyen
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

English and Chinese, known as resource-rich languages, have witnessed the strong development of transformer-based language models for natural language processing tasks. Although Vietnam has approximately 100M people speaking Vietnamese, several pre-trained models, e.g., PhoBERT, ViBERT, and vELECTRA, performed well on general Vietnamese NLP tasks, including POS tagging and named entity recognition. These pre-trained language models are still limited to Vietnamese social media tasks. In this paper, we present the first monolingual pre-trained language model for Vietnamese social media texts, ViSoBERT, which is pre-trained on a large-scale corpus of high-quality and diverse Vietnamese social media texts using XLM-R architecture. Moreover, we explored our pre-trained model on five important natural language downstream tasks on Vietnamese social media texts: emotion recognition, hate speech detection, sentiment analysis, spam reviews detection, and hate speech spans detection. Our experiments demonstrate that ViSoBERT, with far fewer parameters, surpasses the previous state-of-the-art models on multiple Vietnamese social media tasks. Our ViSoBERT model is available only for research purposes. Disclaimer: This paper contains actual comments on social networks that might be construed as abusive, offensive, or obscene.


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NLP@UIT at FigLang-EMNLP 2022: A Divide-and-Conquer System For Shared Task On Understanding Figurative Language
Khoa Thi-Kim Phan | Duc-Vu Nguyen | Ngan Luu-Thuy Nguyen
Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)

This paper describes our submissions to the EMNLP 2022 shared task on Understanding Figurative Language as part of the Figurative Language Workshop (FigLang 2022). Our systems based on pre-trained language model T5 are divide-and-conquer models which can address both two requirements of the task: 1) classification, and 2) generation. In this paper, we introduce different approaches in which each approach we employ a processing strategy on input model. We also emphasize the influence of the types of figurative language on our systems.


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Span Detection for Aspect-Based Sentiment Analysis in Vietnamese
Kim Nguyen Thi Thanh | Sieu Huynh Khai | Phuc Pham Huynh | Luong Phan Luc | Duc-Vu Nguyen | Kiet Nguyen Van
Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation

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Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-stage Span Labeling
Duc-Vu Nguyen | Linh-Bao Vo | Ngoc-Linh Tran | Kiet Nguyen | Ngan Nguyen
Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation


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NLP@UIT at SemEval-2019 Task 4: The Paparazzo Hyperpartisan News Detector
Duc-Vu Nguyen | Thin Dang | Ngan Nguyen
Proceedings of the 13th International Workshop on Semantic Evaluation

This paper describes the system of NLP@UIT that participated in Task 4 of SemEval-2019. We developed a system that predicts whether an English news article follows a hyperpartisan argumentation. Paparazzo is the name of our system and is also the code name of our team in Task 4 of SemEval-2019. The Paparazzo system, in which we use tri-grams of words and hepta-grams of characters, officially ranks thirteen with an accuracy of 0.747. Another system of ours, which utilizes trigrams of words, tri-grams of characters, trigrams of part-of-speech, syntactic dependency sub-trees, and named-entity recognition tags, achieved an accuracy of 0.787 and is proposed after the deadline of Task 4.