Papers by Kiet Nguyen
ViSoBERT: A Pre-Trained Language Model for Vietnamese Social Media Text Processing (2023.emnlp-main)
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| Challenge: | English and Chinese have seen the strong development of transformer-based language models for natural language processing tasks. |
| Approach: | They present a monolingual pre-trained language model for Vietnamese social media texts . they explore emotion recognition, hate speech detection, sentiment analysis, spam reviews detection . |
| Outcome: | The proposed model outperforms the existing models on Vietnamese social media tasks with fewer parameters. |
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. |
M3D: MultiModal MultiDocument Fine-Grained Inconsistency Detection (2024.emnlp-main)
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| Challenge: | Existing methods for fact-checking claim are limited by ambiguous information and lack sample-level predictions. |
| Approach: | They propose a method that predicts the logical relationship of each aspect of a claim from a set of multimodal documents. |
| Outcome: | The proposed method outperforms existing models on two benchmarks while providing finer-grained predictions, explanations, and evidence. |
Multi-Dialect Vietnamese: Task, Dataset, Baseline Models and Challenges (2024.emnlp-main)
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| Challenge: | Vietnamese is a low-resource language, but each province has its own distinct pronunciation variations. |
| Approach: | They propose a dataset that captures the rich diversity of 63 provincial dialects spoken in Vietnam. |
| Outcome: | The proposed dataset captures the rich diversity of 63 provincial dialects spoken across Vietnam. |
ViLexNorm: A Lexical Normalization Corpus for Vietnamese Social Media Text (2024.eacl-long)
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| Challenge: | Lexical normalization is a fundamental task in Natural Language Processing (NLP) it involves the transformation of words into their canonical forms. |
| Approach: | They present a corpus of Vietnamese words annotated by human annotators for the Vietnamese lexical normalization task. |
| Outcome: | The best-performing system achieved 57.74% using the Error Reduction Rate (ERR) metric with the Leave-As-Is (LAI) baseline. |