Enhancing Pre-trained Chinese Character Representation with Word-aligned Attention
Table 3: Evaluation results regarding each model on different datasets. Bold marks highest number among all models. Numbers in brackets indicate the absolute increase over baseline models. Superscript number{}^{1,2,3,4} respectively represents that the corresponding dataset is also used by BERT BERT, BERT-wwm BERTwwm; BERTwwmreport, ERNIE ERNIE and Glyce Glyce, respectively. The results of all baselines are produced by our implementation or retrieved from original papers, and we report the higher one among them. The improvements over baselines are statistically significant (p<0.05). {}^{\dagger} denotes the results of previous state-of-the-art models on these datasets without using BERT.
['Task Dataset', 'SC ChnSenti{}^{2,3}', 'SC weibo-100k{}^{2}', 'NER Ontonotes{}^{4}', 'SPM LCQMC{}^{2,3,4}', 'NLI XNLI{}^{1,2,3,4}', 'MRC DRCD{}^{2,3} [EM|F1]', 'MRC DRCD{}^{2,3} [EM|F1]']
Generally, our method consistently outperforms all baselines on all five tasks, which demonstrates the effectiveness and universality of the proposed approach. Moreover, the Wilcoxon’s test shows that a significant difference (p<0.05) exits between our model and baseline models.