Text-based Question Answering from Information Retrieval and Deep Neural Network Perspectives: A Survey
It synthesizes existing research for practitioners and researchers in natural language processing, but is incremental as it does not propose new methods.
This survey provides a comprehensive overview of text-based question answering models, covering both traditional information retrieval techniques and recent deep neural network approaches, and compares their results using well-known datasets.
Text-based Question Answering (QA) is a challenging task which aims at finding short concrete answers for users' questions. This line of research has been widely studied with information retrieval techniques and has received increasing attention in recent years by considering deep neural network approaches. Deep learning approaches, which are the main focus of this paper, provide a powerful technique to learn multiple layers of representations and interaction between questions and texts. In this paper, we provide a comprehensive overview of different models proposed for the QA task, including both traditional information retrieval perspective, and more recent deep neural network perspective. We also introduce well-known datasets for the task and present available results from the literature to have a comparison between different techniques.