A Deep Network Model for Paraphrase Detection in Short Text Messages
Table 7: Experimental results for paraphrase detection on MSRP corpus.
['[BOLD] Model', '[BOLD] Accuracy', '[BOLD] F1']
As with the Twitter dataset, we also compared our approach with several related methods. The experimental results show that the proposed approach outperforms all the related methods, except quite recent method by Wang et al. Nevertheless, the large number of options introduced in the final model, such as: the semantic matching functions {max, global, local-l}, decomposition operations {rigid, linear, orthogonal} and filter types {unigrams, bigrams, trigrams}, makes it less applicable to re-implement or scale for other datasets or other similar problems. In contrast, we have developed our approach to be more robust and generic, such that it can easily be applied for other datasets.