递归神经网络用于自然语言生成的实验比较

Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02. Pub Date : 2002-11-18 DOI:10.1109/ICONIP.2002.1198155

H. Nakagama, S. Tanaka

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摘要

我们研究了三种类型的递归神经网络(RNN)在自然语言句子生成中的性能:简单递归网络(SRN)、时间反向传播(BPTT)和顺序递归自关联记忆(SRAAM)。我们用简单和复杂的语法来比较学习和扩展的能力。其中，SRAAM在训练和生成相当复杂和较长的句子方面表现最好。

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An experimental comparison of recurrent neural network for natural language production

We study the performance of three types of recurrent neural networks (RNN) for the production of natural language sentences: Simple Recurrent Networks (SRN), Back-Propagation Through Time (BPTT) and Sequential Recursive Auto-Associative Memory (SRAAM). We used simple and complex grammars to compare the ability of learning and being scaled up. Among them, SRAAM is found to have highest performance of training and producing fairly complex and long sentences.

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Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02.

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