Product Description Document Generation Based On Depth Model

With the continuous improvement of people's living standards and the continuous development of science and technology,people shop online more and more frequently.On e-commerce platforms,high-quality product descriptions can provide consumers with better shopping experience.In the absence of offline access to goods,an accurate and attractive description can not only help customers make informed decisions,but also improve the likelihood of a purchase.But for a site like Alibaba with billions of product data,the productivity of human copywriters can't keep pace with the growth of new products.Therefore,the research on the generation of commodity copywriting is of great significance.According to the above mentioned commodity copy generation problem,this thesis will study the generation of intelligent copy.Based on Seq2seq With Attention model,we propose a more perfect BERT-PGN+Weight tying model to generate commodity document.First of all,since the commodity copy generated by Seq2seq With Attention model has problems of unknown words and repeated values,pointer network and overlay mechanism are added to build Pointer Generation Network on the basis of this model.For the problem of unregistered words predicted by Seq2seq With Attention model,pointer network is beneficial to copy words from source text sequence.For the duplicate value problem,the use of an overlay mechanism to track the content of the generated copy can constantly update the attention,thus preventing the generated product copy from repeating itself.Secondly,the BERT pretraining language model is introduced into the pointer generation network as the word embedding layer,so that the text sequence can be processed by the pretraining layer to obtain semantically rich semantic vector containing context features.Finally,due to the Exposure bias problem in Seq2seq model,Weight tying and Scheduled sampling optimization methods are compared based on BERT-PGN model to select the optimal optimization method.Thus,the product description copy generated is better.In this thesis,experiments are carried out on the data set provided by Aliyun to describe the generation,and the ROUGE function is taken as the main evaluation index of the model generated copy.The results show that BEET-PGN model has better effect than Seq2seq With Attention model and Pointer Generator Networks model.Secondly,on the basis of BEET-PGN model,Weight tying and Scheduled sampling are optimized and compared,and the ROUGE score of BEET-PGN+Weight tying is higher than that of Scheduled sampling.Therefore,a comprehensive comparison of the above results shows that BEET-PGN+Weight tying model improves the performance of the product document generation model and has certain reference value for the study of the product document generation.