Character-Aware Convolutional Neural Networks for Paraphrase Identification

Convolutional Neural Network (CNN) have been successfully used for many natural language processing applications. In this paper, we propose a novel CNN model for sentence-level paraphrase identification. We learn the sentence representations using character-aware convolutional neural network that relies on character-level input and gives sentence-level representation. Our model adopts both random and one-hot initialized methods for character representation and trained with two paraphrase identification corpora including news and social media sentences. A comparison between the results of our approach and the typical systems participating in challenge on the news sentence, suggest that our model obtains a comparative performance with these baselines. The experimental result with tweets corpus shows that the proposed model has a significant performance than baselines. The results also suggest that character inputs are effective for modeling sentences.