自组织分层Softmax
Self-organized Hierarchical Softmax
摘要：
我们提出了一个新的自组织分层softmax制定基于神经网络的语言模型在大词汇。而不是使用一个预定义的层次结构，我们的方法是能够学习单词集群具有明确的语法和语义意义的语言模型训练过程中。我们提供了语言建模和句子压缩任务的标准基准测试实验。我们发现这种方法与其他有效的softmax近似一样快，同时相对于类似的完整softmax模型实现了相当甚至更好的性能。
英文摘要：
We propose a new self-organizing hierarchical softmax formulation for neural-network-based language models over large vocabularies. Instead of using a predefined hierarchical structure, our approach is capable of learning word clusters with clear syntactical and semantic meaning during the language model training process. We provide experiments on standard benchmarks for language modeling and sentence compression tasks. We find that this approach is as fast as other efficient softmax approximations, while achieving comparable or even better performance relative to similar full softmax models.
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