Existing methods for text generation usually fed the overall sentiment polarity of a product as an input into the seq2seq model to generate a relatively fluent review. However, these methods cannot express more fine-grained sentiment polarity. Although some studies attempt to generate aspect-level sentiment controllable reviews, the personalized attribute of reviews would be ignored. In this paper, a hierarchical template-transformer model is proposed for personalized fine-grained sentiment controllable generation, which aims to generate aspect-level sentiment controllable reviews with personalized information. The hierarchical structure can effectively learn sentiment information and lexical information separately. The template transformer uses a part of speech (POS) template to guide the generation process and generate a smoother review. To verify our model, we used the existing model to obtain a corpus named FSCG-80 from Yelp, which contains 800K samples and conducted a series of experiments on this corpus. Experimental results show that our model can achieve up to 89.93% aspect-sentiment control accuracy and generate more fluent reviews.

现有的文本生成方法通常将产品的整体情感极性作为输入输入到 seq2seq 模型中，以生成相对流畅的评论。然而，这些方法不能表达更细粒度的情感极性。尽管一些研究试图生成方面级别的情感可控评论，但评论的个性化属性将被忽略。在本文中，提出了一种用于个性化细粒度情感可控生成的分层模板转换器模型，旨在生成具有个性化信息的方面级情感可控评论。层次结构可以有效地分别学习情感信息和词汇信息。模板转换器使用词性 (POS) 模板来指导生成过程并生成更流畅的评论。为了验证我们的模型，我们使用现有模型从 Yelp 获得了一个名为 FSCG-80 的语料库，其中包含 800K 样本，并在该语料库上进行了一系列实验。实验结果表明，我们的模型可以达到高达 89.93% 的aspect-sentiment 控制准确率，并生成更流畅的评论。