Heterogeneous recurrent neural networks for natural language model

Masayuki Tsuji; Teijiro Isokawa; Takayuki Yumoto; Nobuyuki Matsui; Naotake Kamiura

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Heterogeneous recurrent neural networks for natural language model

机译：异质经常性的自然语言模型神经网络

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摘要

Neural networks for language model are proposed and their performances are explored. The proposed network consists of two recurrent networks of which structures are different to each other. Both networks accept words as their inputs, translate their distributed representation, and produce the probabilities of words to occur from their sequence of input words. Performances for the proposed network are investigated through constructions for language models, as compared with a single recurrent neural and a long short-term memory network.

机译：提出了语言模型的神经网络，并探索了他们的表演。所提出的网络由两个经常性网络组成，其中结构彼此不同。两个网络都接受单词作为其输入，转换其分布式表示，并产生从其输入单词序列发生的单词的概率。与语言模型的结构相比，研究了所提出的网络的性能，与单一反复性神经和长短期内存网络相比。

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来源
《Artificial life and robotics》 |2019年第2期|245-249|共5页
作者
Masayuki Tsuji; Teijiro Isokawa; Takayuki Yumoto; Nobuyuki Matsui; Naotake Kamiura;
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作者单位

Graduate School of Engineering University of Hyogo 2167 Shosha Himeji Hyogo 671-2280 Japan Cadence Design Systems 2-100-45 Shin-Yokohama Kohoku-ku Yokohama 222-0033 Japan;

Graduate School of Engineering University of Hyogo 2167 Shosha Himeji Hyogo 671-2280 Japan;

Graduate School of Engineering University of Hyogo 2167 Shosha Himeji Hyogo 671-2280 Japan;

Graduate School of Engineering University of Hyogo 2167 Shosha Himeji Hyogo 671-2280 Japan;

Graduate School of Engineering University of Hyogo 2167 Shosha Himeji Hyogo 671-2280 Japan;

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正文语种 eng
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关键词
Recurrent neural network; Neural probabilistic language model; Heterogeneous network;

机译：经常性神经网络;神经概率语言模型;异质网络;

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专利

1. Heterogeneous recurrent neural networks for natural language model [J] . Masayuki Tsuji, Teijiro Isokawa, Takayuki Yumoto, Artificial life and robotics . 2019,第2期

机译：自然语言模型的异构递归神经网络
2. Parsimonious memory unit for recurrent neural networks with application to natural language processing [J] . Morchid Mohamed Neurocomputing . 2018,第NOVa7期

机译：递归神经网络的简约记忆单元及其在自然语言处理中的应用
3. Learning a bidirectional mapping between human whole-body motion and natural language using deep recurrent neural networks [J] . Plappert Matthias, Mandery Christian, Asfour Tamim Robotics and Autonomous Systems . 2018,第期

机译：使用深且经常性神经网络学习人类全身运动和自然语言之间的双向映射
4. Modeling Local Dependence in Natural Language with Multi-Channel Recurrent Neural Networks [C] . Chang Xu, Weiran Huang, Hongwei Wang, AAAI Conference on Artificial Intelligence . 2019

机译：用多通道复发性神经网络建模本地依赖性的自然语言
5. Deep Neural Language Model for Text Classification Based on Convolutional and Recurrent Neural Networks [D] . Hassan, Abdalraouf. 2018

机译：基于卷积神经网络和递归神经网络的深度神经语言文本分类模型
6. A study of Generalizability of Recurrent Neural Network-Based Predictive Models for Heart Failure Onset Risk using a Large and Heterogeneous EHR Data set [O] . Laila R Bekhet, Yonghui Wu, Ningtao Wang, -1

机译：基于大型和异构EHR数据集的基于递归神经网络的心衰发作风险预测模型的可推广性研究
7. Joint Online Spoken Language Understanding and Language Modeling with Recurrent Neural Networks [O] . Liu, Bing, Lane, Ian 2016

机译：联合在线口语语言理解与语言建模递归神经网络
8. Natural Language Video Description using Deep Recurrent Neural Networks. [R] . Venugopalan, S. 2015

机译：使用深度递归神经网络的自然语言视频描述。

Heterogeneous recurrent neural networks for natural language model

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