  We apply a general deep learning framework to address the non-factoid
question answering task. Our approach does not rely on any linguistic tools and
can be applied to different languages or domains. Various architectures are
presented and compared. We create and release a QA corpus and setup a new QA
task in the insurance domain. Experimental results demonstrate superior
performance compared to the baseline methods and various technologies give
further improvements. For this highly challenging task, the top-1 accuracy can
reach up to 65.3% on a test set, which indicates a great potential for
practical use.
