An Experimental Study of LSTM Encoder-Decoder Model for Text Simplification
Table 1: Reverse Sequence
['V', 'H', 'E', 'Data', 'Train,Val,Test']
We first conduct experiments to show that the LSTM Encoder-Decoder can reverse a sequence after training on a large set of sequence pairs (X,Y), where X = (x1,x2,... ,x25) Y = (x25,x24,...,x1) xi∈V V,H,E represent the vocabulary size, the number of hidden neurons in the LSTM layer, and the training epoch, respectively. The size of our training data is extremely important for the model. The size of the vocabulary also influences the performance. A larger vocabulary requires more training data and more hidden neurons in the LSTM layers. By increasing the number of neurons in the LSTM layers from 128 to 256, we produce a higher capacity model with more neurons that can be trained with fewer epochs and achieve a higher accuracy.