A Multi-cascaded Model with Data Augmentation for Enhanced Paraphrase Detection in Short Texts
Table 8: Comparison of our model’s performance with previously published performances on MSRP dataset
['Model', 'Accuracy', 'F1-score']
The current state of the art on MSRP is reported to be in ji2013discriminative. They report their best results as 80.4% accuracy and an F1-score of 85.9%. However, they assume that they have access to testing data at the time of training a model and call it a form of transductive learning. On the other hand, our model is based on inductive learning where training is done in total isolation from the test split. Therefore, it is only fair to compare both models in inductive setup. The training of the model by ji2013discriminative in inductive setup yields 77.8% accuracy and F1-score of 84.3% agarwal2018deep. These results are far less than what were originally reported in ji2013discriminative using transductive learning. Thus, the current state-of-the-art results in inductive setup are reported by he2015multi and wang2016sentence, with an F1-score of 84.7%. Our best model yields an F1-score of 84.8%, which is slightly higher than previous state-of-the-art.