Fine-Grained Text Sentiment Transfer via Dependency Parsing

Fine-grained sentiment transfer demands to edit an input sentence on a given sentiment intensity while preserving its content, which largely extends traditional binary sentiment transfer. Previous works on sentiment transfer usually attempt to learn latent content representation disentangled from sentiment. However, it is difficult to completely separate these two factors and it is also not necessary. In this paper, we propose a novel model that learns the latent representation without disentanglement and leverages sentiment intensity as input to decoder for fine-grained sentiment control. Moreover, aligned sentences with the same content but different sentiment intensities are usually unavailable. Due to the lack of parallel data, we construct pseudo-parallel sentences (i.e, sentences with similar content but different intensities) to relieve the burden of our model. In specific, motivated by the fact that the sentiment word (e.g., "delicious") has a close relationship with the non-sentiment context word (e.g., "food"), we use dependency parsing to capture the dependency relationship. The pseudo-parallel sentences are produced by replacing the sentiment word with a new one according to the specific context word. Besides, the difference between pseudo-parallel sentences and generated sentences and other constraints are utilized to guide the model precisely revising sentiment. Experiments on the Yelp dataset show that our method substantially improves the degree of content preservation and sentiment accuracy and achieves state-of-the-art performance.