A recent “third wave” of neural network (NN) approaches now delivers state-of-the-art performance in many machine learning tasks, spanning speech recognition, computer vision, and natural language processing. Because these modern NNs often comprise multiple interconnected layers, work in this area is often referred to as deep learning. Recent years have witnessed an explosive growth of research into NN-based approaches to information retrieval (IR). A significant body of work has now been created. In this paper, we survey the current landscape of Neural IR research, paying special attention to the use of learned distributed representations of textual units. We highlight the successes of neural IR thus far, catalog obstacles to its wider adoption, and suggest potentially promising directions for future research.

中文翻译：

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神经信息检索：早年末

神经网络（NN）方法的最新“第三波”现在在许多机器学习任务中提供了最先进的性能，涵盖了语音识别，计算机视觉和自然语言处理。由于这些现代NN通常包含多个相互连接的层，因此在该领域的工作通常被称为深度学习。近年来，目睹了基于NN的信息检索（IR）方法研究的爆炸性增长。现在已经创建了大量工作。在本文中，我们调查了神经IR的现状研究，特别注意使用学习的文本单位的分布式表示形式。我们着重介绍了到目前为止，神经IR的成功，列出了其广泛采用的障碍，并为未来的研究提供了潜在的有希望的方向。