  We explore the applicability of machine translation evaluation (MTE) methods
to a very different problem: answer ranking in community Question Answering. In
particular, we adopt a pairwise neural network (NN) architecture, which
incorporates MTE features, as well as rich syntactic and semantic embeddings,
and which efficiently models complex non-linear interactions. The evaluation
results show state-of-the-art performance, with sizeable contribution from both
the MTE features and from the pairwise NN architecture.
