Deep learning methods employ multiple processing layers to learn hierarchical representations of data, and have produced state-of-the-art results in many domains. Recently, a variety of model designs and methods have blossomed in the context of natural language processing (NLP). In this paper, we review significant deep learning related models and methods that have been employed for numerous NLP tasks and provide a walk-through of their evolution. We also summarize, compare and contrast the various models and put forward a detailed understanding of the past, present and future of deep learning in NLP.
翻译:深层学习方法采用多种处理层来学习数据分级表述,并在许多领域产生了最新成果;最近,各种模型设计和方法在自然语言处理中蓬勃发展;在本文件中,我们审查了许多与深层学习有关的模式和方法,这些模式和方法用于自然语言处理的多项任务,并提供了它们的演变过程。我们还总结、比较和比较了各种模型,并提出了对国家语言处理中过去、现在和未来的深层学习的详细理解。