  This paper explores domain adaptation for enabling question answering (QA)
systems to answer questions posed against documents in new specialized domains.
Current QA systems using deep neural network (DNN) technology have proven
effective for answering general purpose factoid-style questions. However,
current general purpose DNN models tend to be ineffective for use in new
specialized domains. This paper explores the effectiveness of transfer learning
techniques for this problem. In experiments on question answering in the
automobile manual domain we demonstrate that standard DNN transfer learning
techniques work surprisingly well in adapting DNN models to a new domain using
limited amounts of annotated training data in the new domain.
