  Building automatic technical support system is an important yet challenge
task. Conceptually, to answer a user question on a technical forum, a human
expert has to first retrieve relevant documents, and then read them carefully
to identify the answer snippet. Despite huge success the researchers have
achieved in coping with general domain question answering (QA), much less
attentions have been paid for investigating technical QA. Specifically,
existing methods suffer from several unique challenges (i) the question and
answer rarely overlaps substantially and (ii) very limited data size. In this
paper, we propose a novel framework of deep transfer learning to effectively
address technical QA across tasks and domains. To this end, we present an
adjustable joint learning approach for document retrieval and reading
comprehension tasks. Our experiments on the TechQA demonstrates superior
performance compared with state-of-the-art methods.
