SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge
Pei Ke, Haozhe Ji, Siyang Liu, Xiaoyan Zhu, Minlie Huang
Abstract
Most of the existing pre-trained language representation models neglect to consider the linguistic knowledge of texts, which can promote language understanding in NLP tasks. To benefit the downstream tasks in sentiment analysis, we propose a novel language representation model called SentiLARE, which introduces word-level linguistic knowledge including part-of-speech tag and sentiment polarity (inferred from SentiWordNet) into pretrained models. We first propose a contextaware sentiment attention mechanism to acquire the sentiment polarity of each word with its part-of-speech tag by querying SentiWord-Net. Then, we devise a new pre-training task called label-aware masked language model to construct knowledge-aware language representation. Experiments show that SentiLARE obtains new state-of-the-art performance on a variety of sentiment analysis tasks 1 .