Papers by Ngan Nguyen

4 papers
A Vietnamese Dataset for Evaluating Machine Reading Comprehension (2020.coling-main)

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Challenge: despite the lack of benchmark datasets for Vietnamese, there are few studies on machine reading comprehension (MRC) . MRC is an essential core for a range of natural language processing applications such as search engines and intelligent agents.
Approach: They propose to use Vietnamese Question Answering Dataset to evaluate machine reading comprehension in Vietnamese . they use over 23,000 human-generated question-answer pairs based on 5,109 Vietnamese articles .
Outcome: The proposed dataset includes over 23,000 human-generated question-answer pairs based on 5,109 passages of 174 Vietnamese articles from Wikipedia.
VlogQA: Task, Dataset, and Baseline Models for Vietnamese Spoken-Based Machine Reading Comprehension (2024.eacl-long)

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Challenge: Existing datasets for machine reading comprehension tasks in Vietnamese focus on written documents, such as Wikipedia articles, online newspapers, or textbooks.
Approach: They propose to capture Vietnamese spoken language in natural settings and use it to create a machine-learning corpus for machine reading comprehension tasks.
Outcome: The proposed corpus consists of 10,076 question-answer pairs based on 1,230 transcript documents sourced from YouTube .
VLUE: A New Benchmark and Multi-task Knowledge Transfer Learning for Vietnamese Natural Language Understanding (2024.findings-naacl)

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Challenge: a lack of standard evaluation metrics and benchmarks makes it difficult to identify strengths of Vietnamese NLP models.
Approach: They propose to establish a standardized set of benchmarks for Vietnamese NLU . they propose to evaluate Vietnamese language understanding models using a pre-trained model .
Outcome: The proposed model combines proficiency of a multilingual pre-trained model with Vietnamese linguistic knowledge.
Z-GMOT: Zero-shot Generic Multiple Object Tracking (2024.findings-naacl)

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Challenge: Existing approaches to Multi-Object Tracking (MOT) rely on initial bounding boxes and struggle with unseen objects.
Approach: They propose a cutting-edge multi-object tracking solution that can track unseen objects . they propose iGLIP and MA-SORT, which integrate motion and appearance matching strategies .
Outcome: The proposed solution can track objects from never-seen categories without initial bounding boxes or predefined categories.

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