快速最大熵训练的类
Classes for Fast Maximum Entropy Training
摘要：
最大熵模型被许多人认为是语言建模研究中最有前途的途径之一。不幸的是，长时间的训练使得最大熵研究变得困难。我们提出了一种新的加速技术：我们改变了模型的形式，使用类。我们的加速是通过创建两个最大熵模型来实现的，第一个模型预测每个单词的类别，第二个模型预测单词本身。模型的这种分解导致更少的非零指示函数和更快的归一化，与以前最好的技术之一相比，实现了高达35倍的加速。它还导致通常稍低的困惑。同样的技巧也可以用来加速其他机器学习技术的训练，例如神经网络，应用于任何具有大量输出的问题，如语言建模。
英文摘要：
Maximum entropy models are considered by many to be one of the most promising avenues of language modeling research. Unfortunately, long training times make maximum entropy research difficult. We present a novel speedup technique: we change the form of the model to use classes. Our speedup works by creating two maximum entropy models, the first of which predicts the class of each word, and the second of which predicts the word itself. This factoring of the model leads to fewer non-zero indicator functions, and faster normalization, achieving speedups of up to a factor of 35 over one of the best previous techniques. It also results in typically slightly lower perplexities. The same trick can be used to speed training of other machine learning techniques, e.g. neural networks, applied to any problem with a large number of outputs, such as language modeling.
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