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README.md

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+## SPT-ABSA
+
+We continue to pre-train BERT-base via Sentiment-enhance pre-training (SPT).
+
+- Title: An Empirical Study of Sentiment-Enhanced Pre-Training for Aspect-Based Sentiment Analysis
+- Author: Yice Zhang, Yifan Yang, Bin Liang, Shiwei Chen, Bing Qin, and Ruifeng Xu
+- Conference: ACL-2023 Finding (Long)
+
+GitHub Repository: https://github.com/HITSZ-HLT/SPT-ABSA
+
+### What Did We Do?
+
+Aspect-Based Sentiment Analysis (ABSA) is an important problem in sentiment analysis.
+Its goal is to recognize opinions and sentiments towards specific aspects from user-generated content.
+Many research efforts leverage pre-training techniques to learn sentiment-aware representations and achieve significant gains in various ABSA tasks.
+We conduct an empirical study of SPT-ABSA to systematically investigate and analyze the effectiveness of the existing approaches.
+
+We mainly concentrate on the following questions: 
+- (a) what impact do different types of sentiment knowledge have on downstream ABSA tasks?;
+- (b) which knowledge integration method is most effective?; and
+- (c) does injecting non-sentiment-specific linguistic knowledge (e.g., part-of-speech tags and syntactic relations) into pre-training have positive impacts?
+
+Based on the experimental investigation of these questions, we eventually obtain a powerful sentiment-enhanced pre-trained model.
+The powerful sentiment-enhanced pre-trained model is zhang-yice/spt-absa-bert-400k, which integrates three types of knowledge:
+- aspect words: masking aspects' context and predicting them.
+- review's rating score: rating prediction.
+- syntax knowledge: 
+  - part-of-speech,
+  - dependency direction,
+  - dependency distance.
+ 
+### Experimental Results
+
+<img width="75%" alt="image" src="https://github.com/HITSZ-HLT/SPT-ABSA/assets/9134454/38fc2db0-6ccf-47a7-a93c-cf54667e1a23">
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+<img width="75%" alt="image" src="https://github.com/HITSZ-HLT/SPT-ABSA/assets/9134454/20c5a976-014e-433f-a2ec-4bb259e5a382">
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+