Over 97 million people speak Vietnamese as their native language in the world. However, there are few research studies on machine reading comprehension (MRC) for Vietnamese, the task of understanding a text and answering questions related to it. Due to the lack of benchmark datasets for Vietnamese, we present the Vietnamese Question Answering Dataset (UIT-ViQuAD), a new dataset for the low-resource language as Vietnamese to evaluate MRC models. This dataset comprises over 23,000 human-generated question-answer pairs based on 5,109 passages of 174 Vietnamese articles from Wikipedia. In particular, we propose a new process of dataset creation for Vietnamese MRC. Our in-depth analyses illustrate that our dataset requires abilities beyond simple reasoning like word matching and demands single-sentence and multiple-sentence inferences. Besides, we conduct experiments on state-of-the-art MRC methods for English and Chinese as the first experimental models on UIT-ViQuAD. We also estimate human performance on the dataset and compare it to the experimental results of powerful machine learning models. As a result, the substantial differences between human performance and the best model performance on the dataset indicate that improvements can be made on UIT-ViQuAD in future research. Our dataset is freely available on our website 1 to encourage the research community to overcome challenges in Vietnamese MRC.

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Predicting stock prices accurately is a key goal of investors in the stock market. Unfortunately, stock prices are constantly changing and affected by many factors, making the process of predicting them a challenging task. This paper describes a method to build models for predicting stock prices using long short-term memory network (LSTM). The LSTM-based model, which we call dynamic LSTM, is initially built and continuously retrained using newly augmented data to predict future stock prices. We evaluate the proposed method using data sets of four stocks. The results show that the proposed method outperforms others in predicting stock prices based on different performance metrics.

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