  In this paper, we developed a deep neural network (DNN) that learns to solve
simultaneously the three tasks of the cQA challenge proposed by the
SemEval-2016 Task 3, i.e., question-comment similarity, question-question
similarity and new question-comment similarity. The latter is the main task,
which can exploit the previous two for achieving better results. Our DNN is
trained jointly on all the three cQA tasks and learns to encode questions and
comments into a single vector representation shared across the multiple tasks.
The results on the official challenge test set show that our approach produces
higher accuracy and faster convergence rates than the individual neural
networks. Additionally, our method, which does not use any manual feature
engineering, approaches the state of the art established with methods that make
heavy use of it.
