DocumentCode :
540204
Title :
Application of temporal supervised learning algorithm to generation of natural language
Author :
Kamimura, Ryotaro
fYear :
1990
fDate :
17-21 June 1990
Firstpage :
201
Abstract :
An attempt is made to generate natural language by using a recurrent neural network with the temporal supervised learning algorithm (TSLA), developed by R.J. Williams and D. Zipser (1989). As TSLA uses explicit representation of consecutive events, it can deal with time-changing phenomena without increasing the number of units in the network. However, its performance has been evaluated exclusively upon the limited short sequences or sequences with explicit regularity and not for the sequences of natural language, which show complex and long-distance correlation. It was found that TSLA showed extreme instability in the learning process, and it took a long time to finish the learning. Thus, the author proposes two methods to improve the performance of TSLA. The first is the variable learning rate method, which is used to remove the instability of the learning process. The second is Minkowski-r power metrics, which is used to improve the learning time
Keywords :
learning systems; natural languages; neural nets; Minkowski-r power metrics; instability; natural language generation; performance; recurrent neural network; temporal supervised learning algorithm;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1990., 1990 IJCNN International Joint Conference on
Conference_Location :
San Diego, CA, USA
Type :
conf
DOI :
10.1109/IJCNN.1990.137570
Filename :
5726530
Link To Document :
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