UATR: An Uncertainty Aware Two-Stage Refinement Model for Targeted Sentiment Analysis

Target sentiment analysis aims to predict the fine-grained sentiment polarity of a given term. Although some achievements have been made in recent years, the accuracy of targeted sentiment multi-classification technology is still insufficient-a considerable proportion of samples are incorrectly predicted as the opposite polarity. To this end, we investigate the effectiveness of utilizing model uncertainty and propose a two-stage refinement predicting model based on uncertainty called UATR. UATR can model uncertainty by inferring the distribution of model weights and is more robust to small data learning. Experiments on standard benchmark SemEval14 show that our model can not only reduce the proportion of samples incorrectly predicted as the opposite polarity, but also improves accuracy and F1 values by more than 2% and 3% compared to the current state-of-the-art models, respectively.