A Deep Network Model for Paraphrase Detection in Short Text Messages
Table 6: Results on MSRP dataset.
['[BOLD] Model', '[BOLD] Accuracy', '[BOLD] F1']
Firstly, we extract the coarse-grained sentence-level features with the SentMod Architecture and further feed to train paraphrase detection model. Next, we evaluate the pair-wise features to train the paraphrase detection model, these features individually provide 81.9% F1-score. Further, we fuse these pair-wise features with the sentence-level features extracted using the SentMod Architecture to train the paraphrase detection model. This DeepParaphrase Architecture provides a significant improvement in the performance. With this deep learning model we obtain the accuracy of 77.0% and F1-score of 84.0%. Here, we see an improvement of 0.5% in F1-score. Overall, the experimental results show that both sentence-level semantic information and word-level similarity information are important for paraphrase detection task.