  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.
