Question Answering with Deep Learning: A Survey
Date Issued
2019-05
Author(s)
Toshevska, Martina
Mirceva, Georgina
Abstract
Automatically generating answer for a given question is a process in which the computer is supposed to answer a question in a natural language where the question itself is also provided in natural language. Deep learning techniques gained extensive research in both fields of computer vision and natural language processing. Therefore, they are extensively applied for the task of question answering using wide varieties of datasets. This survey aims to overview some of the latest algorithms and models proposed in the field, as well as datasets exploited for training and evaluating the models. In this survey, the models are presented as part of one of the following groups: classical deep neural networks, dynamic memory networks and relation networks. Several datasets have been proposed specifically for the research on automatic question answering. This survey briefly overviews datasets for two different categories of question answering: textual and visual. In the end, evaluation metrics utilized in the field are presented, grouped as: metrics for evaluation of an information retrieval system and metrics for evaluating automatically generated text.
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