A Survey of Knowledge Based Question Answering with Deep Learning

Abstract

The purpose of automated question answering is to let the machine understand natural language questions and give accurate answers in the form of natural language. This technology requires the machine to store a large amount of background knowledge. In recent years, the rapid development of knowledge graph has made the knowledge based question answering (KBQA) more and more popular. Traditional styles of KBQA methods mainly include semantic parsing, information extraction and vector modeling. With the development of deep learning, KBQA with deep learning has gradually become the mainstream method. This paper introduces the application of deep learning in KBQA mainly from the following aspects: the development history of KBQA, KBQA methods using deep learning, common datasets used in KBQA, the comparison of various methods and the future trend.

Triple Multimodal Cyclic Fusion and Self-Adaptive Balancing for Video Q&A燬ystems

“…However, VQA is more challenging than other multimodal tasks. It requires not only an accurate understanding of the semantics of images and questions, but also an effective reasoning to get correct answer in the form of natural language [15]. Chen et al [16] proposed a Multimodal Encoder-Decoder Attention Networks (MEDAN).…”