A Multi-cascaded Model with Data Augmentation for Enhanced Paraphrase Detection in Short Texts
Table 6: Comparison of our model’s performance with previously published performances on Quora dataset
['Model', 'Accuracy']
Accuracy values are given in this table because previous works report accuracies only. In lan2018neural, 7 different models are re-implemented on several tasks involving sentence pairs. Quora dataset is used to get results for paraphrase detection task. We only include results of best performing model among all 7. They find that Shortcut-Stacked Sentence Encoder Model (SSE) nie2017shortcut performs the best, giving testing accuracy of 87.8%. The previous best accuracy is 88.4% tomar2017neural. Our multi-cascaded model beats this result without any data augmentation with an accuracy of 89.4%. As seen from the table, our enhanced model outperforms all previous results. Ensemble BiMPM model achieves an accuracy of 88.2% accuracy while pt-DECATTchar shows a slightly better performance with an accuracy of 88.4%. In comparison, our model achieves an accuracy of 90.3%, which is almost 2% improvement over the previous best performance. In contrast to Wang:2017:BMM:3171837.3171865, we avoid using ensemble model approach (which is computationally costly). Similarly, contrary to results in tomar2017neural, our results are based on word features only and do not use computationally expensive character-based features.