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Neural Paraphrase Identification of Questions with Noisy Pretraining

Thyago Duque
Oscar Täckström
Jakob Uszkoreit
Proceedings of the First Workshop on Subword and Character Level Models in NLP (2017)


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 (Parikh et al., 2016) 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.