切割递归自动编码器树
Cutting Recursive Autoencoder Trees
摘要：
深度学习模型在自然语言处理中取得了相当大的成功。虽然深层架构产生了有用的表示，导致各种任务的改进，但它们通常很难解释。这使得学习结构的分析特别困难。在本文中，我们依靠实证检验来判断特定结构是否有意义。我们提出了一个半监督递归自动编码器，一个著名的模型，产生文本的结构表示的分析。我们表明，对于某些任务，自动编码器的结构可以显着减少，而不会损失分类精度，我们使用人类的判断来评估所产生的结构。
英文摘要：
Deep Learning models enjoy considerable success in Natural Language Processing. While deep architectures produce useful representations that lead to improvements in various tasks, they are often difficult to interpret. This makes the analysis of learned structures particularly difficult. In this paper, we rely on empirical tests to see whether a particular structure makes sense. We present an analysis of the Semi-Supervised Recursive Autoencoder, a well-known model that produces structural representations of text. We show that for certain tasks, the structure of the autoencoder can be significantly reduced without loss of classification accuracy and we evaluate the produced structures using human judgment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
