Progressive Self-Supervised Attention Learning for Aspect-Level Sentiment Analysis
Table 4: Experimental results on various datasets. We directly cited the best experimental results of MN and TNet reported in Wang et al. (2018); Li et al. (2018). ∗∗ and ∗ means significant at p<0.01 and p<0.05 over the baselines (MN, TNet) on each test set, respectively. Here we conducted 1,000 bootstrap tests Koehn (2004) to measure the significance in metric score differences.
['[BOLD] Model', '[BOLD] LAPTOP Macro-F1', '[BOLD] LAPTOP Accuracy', '[BOLD] REST Macro-F1', '[BOLD] REST Accuracy', '[BOLD] TWITTER Macro-F1', '[BOLD] TWITTER Accuracy']
To enhance the persuasiveness of our experimental results, we also displayed the previously reported scores of MN Wang et al. on the same data set. According to the experimental results, we can come to the following conclusions: