A Multi-cascaded Model with Data Augmentation for Enhanced Paraphrase Detection in Short Texts
Table 10: Comparison of our model’s performance with previously published performances on SemEval dataset
['Model', 'Precision', 'Recall', 'F1-score']
We compare our results with existing work in terms of precision, recall and F1-score On this dataset, lan2018neural in their comparative study of re-implementing 7 different models, found that Pairwise Word Interaction Model (PWIM) he2016pairwise performs the best and achieves F1-score of 65.6%, which we report in the table. State of the art performance on the SemEval dataset is reported by agarwal2018deep with F1-score of 75.1%. Our best performing model outperforms state of the art by achieving an F1-score of 75.4%.