A Multi-cascaded Model with Data Augmentation for Enhanced Paraphrase Detection in Short Texts
Table 5: Paraphrase detection performance on Quora 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 (accuracy, precision, recall, and F1-score) on test sets are given for different data augmentation steps with learned features and learned plus linguistic features. We first discuss results obtained by using learned features only. Using the original data without any augmentation, our model achieves an accuracy of 89.4%. Augmenting the data with step P2 and NP1 (paraphrase and non-paraphrase generation by symmetry) increases the accuracy slightly. Note that this augmentation step has also been performed in earlier works tomar2017neural; agarwal2018deep. However, noticeable increase in accuracy is observed when the data is augmented with additional paraphrases using step P2, step P3 (generation by transitive closure), and step P1 (generation by reflexivity), jumping the accuracy to 90.2%. We obtain the best performance of 90.3% when in addition to augmenting paraphrases via steps P3, P2, and P1 additional non-paraphrases are generated via step NP1. This is also the current state-of-the-art performance on this dataset.