Search

Recently, Chinese poetry generation gains many significant achievement with the development of deep learning. However, existing methods can not generate Chinese palindrome poetry. Besides, there is no public dataset of Chinese palindrome poetry. In this paper, we propose a novel Chinese palindrome poetry generation model, named Chinese Palindrome Poetry Generation Model (CPPGM), based on the universal seq2seq model and language model with specific beam search algorithms. In addition, the proposed model is the first to generate Chinese palindrome poetry automatically, and is applicable to other palindromes, such as palindrome couplets. Compared with several methods we propose, the experimental results demonstrate the superiority of CPPGM with machine evaluation as well as human judgment.

Restricted Boltzmann machines (RBMs) are widely applied to solve many machine learning problems. Usually, the cost function of RBM is log-likelihood function of marginal distribution of input data, and the training method involves maximizing the cost function. Distribution of the trained RBM is identical to that of input data. But the reconstruction error always exists even the distributions are almost identical. In this paper, a method to train RBM by adding reconstruction error to the cost function is put forward. Two categories of trials are performed to validate the proposed method: feature extraction and classification. The experimental results show that the proposed method can be effective.