Advances in Natural Language Question Answering: A Review
It addresses the problem of handling dynamic language variations in question answering for AI communities, but it is incremental as it is a review paper summarizing existing work.
This paper reviews the evolution of question answering systems, highlighting that deep learning methods have achieved higher results compared to earlier statistical and machine learning approaches, though specific numbers are not provided.
Question Answering has recently received high attention from artificial intelligence communities due to the advancements in learning technologies. Early question answering models used rule-based approaches and moved to the statistical approach to address the vastly available information. However, statistical approaches are shown to underperform in handling the dynamic nature and the variation of language. Therefore, learning models have shown the capability of handling the dynamic nature and variations in language. Many deep learning methods have been introduced to question answering. Most of the deep learning approaches have shown to achieve higher results compared to machine learning and statistical methods. The dynamic nature of language has profited from the nonlinear learning in deep learning. This has created prominent success and a spike in work on question answering. This paper discusses the successes and challenges in question answering question answering systems and techniques that are used in these challenges.