Mutual Disentanglement Learning for Joint Fine-Grained Sentiment Classification and Controllable Text Generation

Fine-grained sentiment classification (FGSC) task and fine-grained controllable text generation (FGSG) task are two representative applications of sentiment analysis, two of which together can actually form an inverse task prediction, i.e., the former aims to infer the fine-grained sentiment polarities given a text piece, while the latter generates text content that describes the input fine-grained opinions. Most of the existing work solves the FGSC and the FGSG tasks in isolation, while ignoring the complementary benefits in between. This paper combines FGSC and FGSG as a joint dual learning system, encouraging them to learn the advantages from each other. Based on the dual learning framework, we further propose decoupling the feature representations in two tasks into fine-grained aspect-oriented opinion variables and content variables respectively, by performing mutual disentanglement learning upon them. We also propose to transform the difficult data-to-text generation fashion widely used in FGSG into an easier text-to-text generation fashion by creating surrogate natural language text as the model inputs. Experimental results on 7 sentiment analysis benchmarks including both the document-level and sentence-level datasets show that our method significantly outperforms the current strong-performing baselines on both the FGSC and FGSG tasks. Automatic and human evaluations demonstrate that our FGSG model successfully generates fluent, diverse and rich content conditioned on fine-grained sentiments.

Automated summary

This paper combines fine-grained sentiment classification and fine-grained controllable text generation as a joint dual learning system, improving the performance of both tasks through mutual learning. By decoupling the feature representations and transforming the difficult data-to-text generation into an easier text-to-text generation, the model's effectiveness is further enhanced.