Apply deep learning to improve the question analysis model in the Vietnamese question answering system

Question answering (QA) system nowadays is quite popular for automated answering purposes, the meaning analysis of the question plays an important role, directly affecting the accuracy of the system. In this article, we propose an improvement for question-answering models by adding more specific question analysis steps, including contextual characteristic analysis, pos-tag analysis, and question-type analysis built on deep learning network architecture. Weights of extracted words through question analysis steps are combined with the best matching 25 (BM25) algorithm to find the best relevant paragraph of text and incorporated into the QA model to find the best and least noisy answer. The dataset for the question analysis step consists of 19,339 labeled questions covering a variety of topics. Results of the question analysis model are combined to train the question-answering model on the data set related to the learning regulations of Industrial University of Ho Chi Minh City. It includes 17,405 pairs of questions and answers for the training set and 1,600 pairs for the test set, where the robustly optimized BERT pre-training approach (RoBERTa) model has an F1-score accuracy of 74%. The model has improved significantly. For long and complex questions, the mode has extracted weights and correctly provided answers based on the question’s contents

Video captioning in Vietnamese using deep learning

With the development of today's society, demand for applications using digital cameras jumps over year by year. However, analyzing large amounts of video data causes one of the most challenging issues. In addition to storing the data captured by the camera, intelligent systems are required to quickly analyze the data to correct important situations. In this paper, we use deep learning techniques to build automatic models that describe movements on video. To solve the problem, we use three deep learning models: sequence-to-sequence model based on recurrent neural network, sequence-to-sequence model with attention and transformer model. We evaluate the effectiveness of the approaches based on the results of three models. To train these models, we use microsoft research video description corpus (MSVD) dataset including 1970 videos and 85,550 captions translated into Vietnamese. In order to ensure the description of the content in Vietnamese, we also combine it with the natural language processing (NLP) model for Vietnamese