Recent progress in leveraging deep learning methods for question answering

Question answering, serving as one of important tasks in natural language processing, enables machines to understand questions in natural language and answer the questions concisely. From web search to expert systems, question answering systems are widely applied to various domains in assisting information seeking. Deep learning methods have boosted various tasks of question answering and have demonstrated dramatic effects in performance improvement for essential steps of question answering. Thus, leveraging deep learning methods for question answering has drawn much attention from both academia and industry in recent years. This paper provides a systematic review of the recent development of deep learning methods for question answering. The survey covers the scope including methods, datasets, and applications. The methods are discussed in terms of network structure characteristics, methodology innovations, and their effectiveness. The survey is expected to be a contribution to the summarization of recent research progress and future directions of deep learning methods for question answering.

Automated summary

This paper provides a systematic review of recent developments in deep learning methods for question answering, covering methods, datasets, and applications, and discussing network structure characteristics, method innovations, and effectiveness. The survey is expected to contribute to summarizing recent research progress and future research directions in deep learning methods for question answering.