Sentence Similarity Learning by Lexical Decomposition and Composition
Table 4: Experimental results for paraphrase identification on MSRP corpus.
['Models', 'Acc', 'F1']
MSRP dataset. \newciteyin2015convolutional employed a CNN model to learn sentence representations on multiple level of granularity and modeled interaction features at each level for a pair of sentences. However, their model heavily depends on the pretraining strategy. \newcitehe2015multi proposed a similar model to \newciteyin2015convolutional. Similarly, they also used a CNN model to extract features at multiple levels of granularity. \newciteyin2015abcnn applied their attention-based CNN model on this dataset. By adding a couple of sparse features and using a layerwise training strategy, they got a pretty good performance. However, the best performance so far on this dataset is obtained by \newciteji2013discriminative. In their model, they just utilized several hand-crafted features in a Support Vector Machine (SVM) model. Therefore, the deep learning methods still have a long way to go for this task.