Search Results for author:
Found 7 papers, 3 papers with code
ViCLEVR: A Visual Reasoning Dataset and Hybrid Multimodal Fusion Model for Visual Question Answering in Vietnamese
Neural models for VQA have made remarkable progress on large-scale datasets, with a primary focus on resource-rich languages like English.
Generative Pre-trained Transformer for Vietnamese Community-based COVID-19 Question Answering
Recent studies have provided empirical evidence of the wide-ranging potential of Generative Pre-trained Transformer (GPT), a pretrained language model, in the field of natural language processing.
BARTPhoBEiT: Pre-trained Sequence-to-Sequence and Image Transformers Models for Vietnamese Visual Question Answering
Visual Question Answering (VQA) is an intricate and demanding task that integrates natural language processing (NLP) and computer vision (CV), capturing the interest of researchers.
A Comparative Study of Question Answering over Knowledge Bases
In that, we study various question types, properties, languages, and domains to provide insights on where existing systems struggle.
Conversational Machine Reading Comprehension for Vietnamese Healthcare Texts
To help machines understand conversation texts, we present UIT-ViCoQA, a new corpus for conversational machine reading comprehension in the Vietnamese language.
UIT-HSE at WNUT-2020 Task 2: Exploiting CT-BERT for Identifying COVID-19 Information on the Twitter Social Network
Recently, COVID-19 has affected a variety of real-life aspects of the world and led to dreadful consequences.
Enhancing lexical-based approach with external knowledge for Vietnamese multiple-choice machine reading comprehension
Although Vietnamese is the 17th most popular native-speaker language in the world, there are not many research studies on Vietnamese machine reading comprehension (MRC), the task of understanding a text and answering questions about it.
