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RCRN: Other Writing Technologies (Topic)最新文献

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Generating Semantic Sentences
Pub Date : 2017-01-07 DOI: 10.2139/ssrn.2951669
Manish Periwal
Given the growing complexity of tasks and data in NLP there are limited unsupervised learning methods to tackle the problems. In this paper we will look into novel approach to one of the problems as representing sentences in latent space using Recurrent Neural Network (RNN) and Variational Auto Encoder. Combining VAE-LSTM approach we will repharase (and generate similar and meaningful) sentences from given sentence. An our assumption same architecture can also be applied to language modeling problem. We will give results on using single layer encoder as well as 2 layer encoder for our VAE.
鉴于NLP中任务和数据的日益复杂,解决这些问题的无监督学习方法有限。在本文中,我们将研究使用递归神经网络(RNN)和变分自编码器来解决潜在空间中句子表示问题之一的新方法。结合vee - lstm方法,我们将从给定的句子中重新措辞(并生成相似且有意义的句子)。我们假设同样的体系结构也可以应用于语言建模问题。我们将给出对VAE使用单层编码器和两层编码器的结果。
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引用次数: 1
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RCRN: Other Writing Technologies (Topic)
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