A Multi-cascaded Model with Data Augmentation for Enhanced Paraphrase Detection in Short Texts
Table 7: Paraphrase detection performance on MSRP 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']
Performances are given for configurations with and without linguistic features after applying various data augmentation steps. It is observed that without data augmentation, we achieve an F1-score of 82.4%. Doubling the data (step P2 and NP1) increases performance slightly in terms of F1-score but the significant gain is obtained when augmenting the data using symmetry, transitivity, and reflexivity (steps P2, P3, P1). This configuration of augmentation gives F1-score of 84.2%. The highest F1-score in experiments without any linguistic features of 84.3% is achieved when P2, P3, P1, and NP1 augmentation steps are performed.