  Duplicate question detection is an ongoing challenge in community question
answering because semantically equivalent questions can have significantly
different words and structures. In addition, the identification of duplicate
questions can reduce the resources required for retrieval, when the same
questions are not repeated. This study compares the performance of deep neural
networks and gradient tree boosting, and explores the possibility of domain
adaptation with transfer learning to improve the under-performing target
domains for the text-pair duplicates classification task, using three
heterogeneous datasets: general-purpose Quora, technical Ask Ubuntu, and
academic English Stack Exchange. Ultimately, our study exposes the alternative
hypothesis that the meaning of a "duplicate" is not inherently general-purpose,
but rather is dependent on the domain of learning, hence reducing the chance of
transfer learning through adapting to the domain.
