Ancient poetry generation based on bidirectional LSTM model neural network

Abstract

Automatic generation of ancient poems has been a research hotspot in the field of artificial intelligence, which is of great significance in cultural inheritance and literary creation. Due to the strict tonal patterns and complex structural rules of classical Chinese poetry, generating classical Chinese poetry has been a challenging task for both human poets and computer programs. In this paper, we propose a neural network model based on the introduction of an attention mechanism, which combines a bidirectional LSTM model and an attention mechanism to solve some problems in the traditional automatic generation model of ancient poems. The model is trained using the dataset of ancient poems studied by previous researchers, and the performance of the model is evaluated and analyzed by evaluating the metrics BLEU, Perplexity, Generation Effect and Linguistic Coherence. The experimental results show that the model exhibits good performance and excellent results on the task of automatic generation of ancient poems.