文章目录 语言模型 RNN语言模型的结构 优化 WSJ 实验 参考文献:《Recurrent neural network based language model》 语言模型 什么是语言模型? 语言模型包括传统与神经网络语言模型 神经网络语言模型的功能就是根据句子前w-1个词预测第w个词在词表中的概率分布。 RNN语言模型的结构 在论文中采用了最简单的RNN架构也...
构建语言模型,就是处理序列预测问题(sequential data prediction)。然而,很多自然语言方法都针对于特定的语言领域(very specific for language domain):假设自然语言可以使用分析树(parse tree)来表示,需要考虑词的形态学(morphology)、语法和语义。即使是基于 n-gram 的最通用的模型,也进行了假设:语言是由原子性的符号...
边缘概率分布 p(Xt=k) 称为 unigram; 使用一阶马尔科夫模型(Markov model),则 p(Xt=k|Xt−1=j) 又称为 bigram; 类似地,基于二阶马尔科夫模型,p...Lecture 6: Language Models and Recurrent Neural Networks 文章目录 Language Modeling n-gram Language Models Sparsity Problems(稀疏问题) Storage ...
Recurrent neural network based language modelExtensions of Recurrent neural network based language modelGenerating Text with Recurrent Neural Networks 机器翻译(Machine Translation) 机器翻译是将一种源语言语句变成意思相同的另一种源语言语句,如将英语语句变成同样意思的中文语句。与语言模型关键的区别在于,...
论文《Recurrent neural network based language model》简称RNNLM,作者Tomas Mikolov,经典的循环/递归神经语言模型。 2. 摘要 提出了一种新的基于递归神经网络的语言模型(RNN LM)及其在语音识别中的应用。 结果表明,与现有的退避语言模型相比,通过使用几个RNN LMs的混合,可以获得大约50%的困惑减少。
Recurrent Neural Network Language Models (RNN-LMs) have recently shown exceptional performance across a variety of applications. In this paper, we modify the architecture to perform Language Understanding, and advance the state-of-the-art for the widely used ATIS dataset. The core of our approach...
Recurrent Neural Network Model Why not a standard model? Problems: Inputs, outputs can be different lengths in different examples. Doesn't share features learned across different positions of text. RNN prediction不仅用到了第t个词,也用到了前面的词的信息 ...
Recurrent neural network based language model Extensions of Recurrent neural network based language ...
Extensions of Recurrent neural network based language model Generating Text with Recurrent Neural Networks 2.2 机器翻译 机器翻译类似于语言模型,输入是一个源语言的词序列(例如,德语)。我们想输出一个目标语言的词序列(例如,英语)。关键的区别就是我们的输出只能在我们已经看见整个输入之后开始,因为,我们翻译句子的...
Li, and T. Schultz, "Recurrent neural network language model- ing for code switching conversational speech," in ICAS- SP2013.H. Adel, N. T. Vu, F. Kraus, T. Schlippe, H. Li, and T. Schultz, "Recurrent neural network language modeling for code switching conversational speech," in ...