An Experimental Study of LSTM Encoder-Decoder Model for Text Simplification
Table 3: Replace in Sequence
['V', 'H', 'E', 'Data', 'Train,Val,Test']
We next show that the LSTM Encoder-Decoder can replace words in a sequence. For the sequence pairs (X,Y), where X = (x1,x2,... ,x25) Y = (y1,y2, ...,y25) yi = ximodn xi,yi∈V We let n=2,20,200 when |V|=10,100,1000. We only keep the top 20 percent of words in the vocabulary, and use these words to replace all matching words in the output sequence. Lexical simplification is similar, in which we can regard the top 20 percent of words in the vocabulary as simple and common words and all the other words are complex words. Similarly, we can also think of the top 20 percent of words as meaningful and important words, other words are redundant. Therefore it also suits the task of removing redundant words. Compared with the sorting operation, the replacement operation is easier to train when the vocabulary is large, or the size of training data is not large.