A Deep Network Model for Paraphrase Detection in Short Text Messages
Table 3: Results on SemEval 2015 Twitter dataset.
['[BOLD] Model', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F1-score']
We train our model using the training dataset with development set for tuning the parameters, and then we test the system with the provided testing dataset of 838 test entries, ignoring the “debatable” entries. Recall that there are mainly two components in the proposed approach: (i) Sentence modelling using CNN and LSTM, and (ii) Pair-wise word similarity matching. Our intuition for using the two models is that both coarse-grained sentence-level and fine-grained word-level information should be important for the paraphrase detection task. In our experiments, we firstly use only sentence modelling architecture to develop the paraphrase detection model. We call this experiment the SentMod Architecture for paraphrase detection. Next, we use the pair-wise word similarity matching model to extract the word-level similarity information-based features. When we use only these features to train the paraphrase model, the model provides an F1-score of 0.702. We call these features the pair-wise features.