  Duplicate question detection (DQD) is important to increase efficiency of
community and automatic question answering systems. Unfortunately, gathering
supervised data in a domain is time-consuming and expensive, and our ability to
leverage annotations across domains is minimal. In this work, we leverage
neural representations and study nearest neighbors for cross-domain
generalization in DQD. We first encode question pairs of the source and target
domain in a rich representation space and then using a k-nearest neighbour
retrieval-based method, we aggregate the neighbors' labels and distances to
rank pairs. We observe robust performance of this method in different
cross-domain scenarios of StackExchange, Spring and Quora datasets,
outperforming cross-entropy classification in multiple cases.
