13 papers
Syllabic-Structure Decoder for Automatic Speech Recognition in Vietnamese
Nghia Hieu Nguyen, Quan Ngoc Hoang, Long Hoang Huu Nguyen +2
Most Automatic Speech Recognition (ASR) systems formulate transcription as a prediction problem over orthographic units such as characters, subwords, or words. Although effective,…
Phonetic Modeling of Dialectal Variation in Vietnamese Speech
Quan Ngoc Hoang, Long Hoang Huu Nguyen, Nghia Hieu Nguyen +2
Vietnamese exhibits substantial dialectal phonetic variation across Northern, Central, and Southern regions, where identical lexical items may be realized with markedly different p…
Linguistically Informed Multimodal Fusion for Vietnamese Scene-Text Image Captioning: Dataset, Graph Framework, and Phonological Attention
Nhi Ngoc-Yen Nguyen, Anh-Duc Nguyen, Nghia Hieu Nguyen +2
Scene-text image captioning requires fusing three information streams -- visual features, OCR-detected text, and linguistic knowledge -- to generate descriptions that faithfully in…
ViGoEmotions: A Benchmark Dataset For Fine-grained Emotion Detection on Vietnamese Texts
Hung Quang Tran, Nam Tien Pham, Son T. Luu +1
Emotion classification plays a significant role in emotion prediction and harmful content detection. Recent advancements in NLP, particularly through large language models (LLMs),…
ViCLSR: A Supervised Contrastive Learning Framework with Natural Language Inference for Natural Language Understanding Tasks
Tin Van Huynh, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen
High-quality text representations are crucial for natural language understanding (NLU), but low-resource languages like Vietnamese face challenges due to limited annotated data. Wh…
ViTextVQA: A Large-Scale Visual Question Answering Dataset and a Novel Multimodal Feature Fusion Method for Vietnamese Text Comprehension in Images
Quan Van Nguyen, Dan Quang Tran, Huy Quang Pham +4
Visual Question Answering (VQA) is a challenging task that requires the joint understanding of natural language and visual content. While early research primarily focused on recogn…