Encoder–Decoder Couplet Generation Model Based on ‘Trapezoidal Context’ Character Vector

Abstract

Abstract This paper studies the couplet generation model which automatically generates the second line of a couplet by giving the first line. Unlike other sequence generation problems, couplet generation not only considers the sequential context within a sentence line but also emphasizes the relationships between the corresponding words of first and second lines. Therefore, a trapezoidal context character embedding the vector model has been developed firstly, which considers the ‘sequence context’ and the ‘corresponding word context’ simultaneously. Afterwards, we chose the typical encoder–decoder framework to solve the sequence–sequence problems, of which the encoder and decoder are used by bi-directional GRU and GRU, respectively. In order to further increase the semantic consistency of the first and second lines of couplets, the pre-trained sentence vector of the first line is added to the attention mechanism in the model. To verify the effectiveness of the method, it is applied to the real data set. Experimental results show that our proposed model can compete with the up-to-date methods, and both adding sentence vectors to attention and using trapezoidal context character vectors can improve the effectiveness of the algorithm.