A Multi-cascaded Model with Data Augmentation for Enhanced Paraphrase Detection in Short Texts
Table 9: Paraphrase detection performance on SemEval dataset
['Augmentation', 'Learned Features Accuracy', 'Learned Features Precision', 'Learned Features Recall', 'Learned Features F1-score', 'Learned + Linguistic Features Accuracy', 'Learned + Linguistic Features Precision', 'Learned + Linguistic Features Recall', 'Learned + Linguistic Features F1-score']
In this table, data augmentation is done with full transitive closure, i.e., K=∗ for step P3. Without data augmentation and linguistic features, our model achieves an F1-score of 40.2%. However, when we apply data augmentation, the F1-score tends to increase. The maximum F1-score without linguistic features is 64.2% which is achieved with P2, P3, P1, and NP1 augmentation. This is consistent with the results of other two datasets used in the study, which also yield maximum performance on this particular augmentation combination. In noisy text, linguistic features affect performance by a large margin.