A sentence style conversion method using LSTM-RNN

Abstract

This paper describes a sentence style conversion method using recurrent neural network with long short-term memory cells (LSTM-RNN). In the proposed method, LSTM-RNN is used to learn direct style sentences vectorized by one-hot expressions. Then, the sentence end expression of the distal style sentence is removed and the vectorized one is input into the learned model. The next word is predicted until sentence ends, and the obtained word vector sequence is added to the end of the input vector sequence. A direct style sentence is converted by decoding the generated vector sequence into the form of the natural language. We experimented to evaluate the accuracy of the proposed method. As a result, it turned out that a sentence style can be converted by the method.