Heterogeneous recurrent neural networks for natural language model
Heterogeneous recurrent neural networks for natural language model
复制标题
用于自然语言模型的异构循环神经网络
DOI：
10.1007/s10015-018-0507-1
复制
发表时间：
2019
影响因子：
0.9
通讯作者：
and N.Kamiura
中科院分区：
文献类型：
--
作者：
M.Tsuji;T.Isokawa;N.Yumoto;N.Matsui;and N.Kamiura
提出了用于语言模型的神经网络并探讨了它们的性能。所提出的网络由两个结构彼此不同的循环网络组成。两个网络都接受单词作为输入，翻译其分布式表示，并根据输入单词序列生成单词出现的概率。与单个循环神经网络和长短期记忆网络相比，通过构建语言模型来研究所提出的网络的性能。
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.