In the field of natural language generation (NLG), controlling text generation (CTG) is critical, particularly in query auto-completion (QAC) where the need for personalization and diversity is paramount. However, it is essentially challenging to adapt to various control objectives and constraints, which results in existing CTG approaches meeting with mixed success. This paper presents UCTG, a unified controllable text generation framework, which introduces a novel prompt learning method for CTG. Specifically, this framework seamlessly integrates a control module, a prompt module, and a generation module. The control module leverages a fine-tuned model to distill user preference features and behavioral patterns from historical data, incorporating human feedback into the model’s loss functions. These features are then transformed by the prompt module into vectors that guide the generation module. As such, the text generation can be flexibly controlled without modifying the task settings. By employing this unified approach, UCTG significantly improves query accuracy and coherence in tasks with different objectives and constraints, which is validated by extensive experiments on the Meituan and AOL real-world datasets. UCTG not only improves text generation control in QAC but also sets a new framework for flexible NLG applications.

Data for the Rating Prediction (RP) sentiment analysis task such as star reviews are readily available. However, data for aspect-category sentiment analysis (ACSA) is often desired because of the fine-grained nature but are expensive to collect. In this work we present a method for learning ACSA using only RP labels. We propose Unified Sentiment Analysis (Uni-SA) to efficiently understand aspect and review sentiment in a unified manner. We propose a Distantly Supervised Pyramid Network (DSPN) to efficiently perform Aspect-Category Detection (ACD), ACSA, and OSA using only RP labels for training. We evaluate DSPN on multi-aspect review datasets in English and Chinese and find that with only star rating labels for supervision, DSPN performs comparably well to a variety of benchmark models. We also demonstrate the interpretability of DSPN’s outputs on reviews to show the pyramid structure inherent in document level end-to-end sentiment analysis.

Sentiment analysis has attracted increasing attention in e-commerce. The sentiment polarities underlying user reviews are of great value for business intelligence. Aspect category sentiment analysis (ACSA) and review rating prediction (RP) are two essential tasks to detect the fine-to-coarse sentiment polarities. ACSA and RP are highly correlated and usually employed jointly in real-world e-commerce scenarios. While most public datasets are constructed for ACSA and RP separately, which may limit the further exploitation of both tasks. To address the problem and advance related researches, we present a large-scale Chinese restaurant review dataset ASAP including 46, 730 genuine reviews from a leading online-to-offline (O2O) e-commerce platform in China. Besides a 5-star scale rating, each review is manually annotated according to its sentiment polarities towards 18 pre-defined aspect categories. We hope the release of the dataset could shed some light on the field of sentiment analysis. Moreover, we propose an intuitive yet effective joint model for ACSA and RP. Experimental results demonstrate that the joint model outperforms state-of-the-art baselines on both tasks.