A short survey on end-to-end simple question answering systems

Searching for a specific and meaningful piece of information in the humongous textual data volumes found on the internet and knowledge repositories is a very challenging task. This problem is usually constrained to answering simple, factoid questions by resorting to a question answering (QA) system built on top of complex approaches such as heuristics, information retrieval, and machine learning. More precisely, deep learning methods became into sharp focus of this research field because such purposes can realize the benefits of the vast amounts of data to boost the practical results of QA systems. In this paper, we present a systematic survey on deep learning-based QA systems concerning factoid questions, with particular focus on how each existing system addresses their critical features in terms of learning end-to-end models. We also detail the evaluation process carried out on these systems and discuss how each approach differs from the others in terms of the challenges tackled and the strategies employed. Finally, we present the most prominent research problems still open in the field.