DTCA: Decision Tree-based Co-Attention Networks for Explainable Claim Verification
Table 3: The performance comparison of models on different number of comments.
['No (0%) Comments', 'No (0%) Comments', 'No (0%) Comments A', 'No (0%) Comments P', 'No (0%) Comments R', 'No (0%) Comments F1']
4.4.1 The impact of comments on DTCA To further investigate the quantitative impact of comments on our model, we evaluate the performance of DTCA and CaSa with 0%, 50%, and 100% comments. We gain the following observations: [leftmargin=*] Models without comment features present the lowest performance, decreasing from 5.08% to 9.76% in accuracy on the two datasets, which implies that there are a large number of veracity-indicative features in comments. As the proportion of comments expands, the performance of models is improved continuously. However, the rate of comments for CaSa raises from 50% to 100%, the boost is not significant, only achieving 1.44% boosts in accuracy on RumourEval, while DTCA obtains better performance, reflecting 3.90% and 3.28% boosts in accuracy on the two datasets, which fully proves that DTCA can choose valuable comments and ignore unimportant comments with the help of DTE.