  In this paper, we propose convolutional neural networks for learning an
optimal representation of question and answer sentences. Their main aspect is
the use of relational information given by the matches between words from the
two members of the pair. The matches are encoded as embeddings with additional
parameters (dimensions), which are tuned by the network. These allows for
better capturing interactions between questions and answers, resulting in a
significant boost in accuracy. We test our models on two widely used answer
sentence selection benchmarks. The results clearly show the effectiveness of
our relational information, which allows our relatively simple network to
approach the state of the art.
