SentiLR: Linguistic Knowledge Enhanced Language Representation for Sentiment Analysis
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Most of the existing pre-trained language representation models neglect to consider the linguistic knowledge of texts, whereas we argue that such knowledge can promote language understanding in various NLP tasks. In this paper, we propose a novel language representation model called SentiLR, which introduces word-level linguistic knowledge including part-of-speech tag and prior sentiment polarity from SentiWordNet to benefit the downstream tasks in sentiment analysis. During pre-training, we first acquire the prior sentiment polarity of each word by querying the SentiWordNet dictionary with its part-of-speech tag. Then, we devise a new pre-training task called label-aware masked language model (LA-MLM) consisting of two subtasks: 1) word knowledge recovering given the sentence-level label; 2) sentence-level label prediction with linguistic knowledge enhanced context. Experiments show that SentiLR achieves state-of-the-art performance on several sentence-level / aspect-level sentiment analysis tasks by fine-tuning, and also obtain comparative results on general language understanding tasks.
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