Answer sentence selection is the task of identifying sentences that contain
the answer to a given question. This is an important problem in its own right
as well as in the larger context of open domain question answering...
a novel approach to solving this task via means of distributed representations,
and learn to match questions with answers by considering their semantic
encoding. This contrasts prior work on this task, which typically relies on
classifiers with large numbers of hand-crafted syntactic and semantic features
and various external resources. Our approach does not require any feature
engineering nor does it involve specialist linguistic data, making this model
easily applicable to a wide range of domains and languages. Experimental
results on a standard benchmark dataset from TREC demonstrate that---despite
its simplicity---our model matches state of the art performance on the answer
sentence selection task.