%0 Conference Proceedings %T Neural Paraphrase Identification of Questions with Noisy Pretraining %A Tomar, Gaurav Singh %A Duque, Thyago %A Täckström, Oscar %A Uszkoreit, Jakob %A Das, Dipanjan %Y Faruqui, Manaal %Y Schuetze, Hinrich %Y Trancoso, Isabel %Y Yaghoobzadeh, Yadollah %S Proceedings of the First Workshop on Subword and Character Level Models in NLP %D 2017 %8 September %I Association for Computational Linguistics %C Copenhagen, Denmark %F tomar-etal-2017-neural %X We present a solution to the problem of paraphrase identification of questions. We focus on a recent dataset of question pairs annotated with binary paraphrase labels and show that a variant of the decomposable attention model (replacing the word embeddings of the decomposable attention model of Parikh et al. 2016 with character n-gram representations) results in accurate performance on this task, while being far simpler than many competing neural architectures. Furthermore, when the model is pretrained on a noisy dataset of automatically collected question paraphrases, it obtains the best reported performance on the dataset. %R 10.18653/v1/W17-4121 %U https://aclanthology.org/W17-4121 %U https://doi.org/10.18653/v1/W17-4121 %P 142-147