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.