  This paper explores the problem of ranking short social media posts with
respect to user queries using neural networks. Instead of starting with a
complex architecture, we proceed from the bottom up and examine the
effectiveness of a simple, word-level Siamese architecture augmented with
attention-based mechanisms for capturing semantic "soft" matches between query
and post tokens. Extensive experiments on datasets from the TREC Microblog
Tracks show that our simple models not only achieve better effectiveness than
existing approaches that are far more complex or exploit a more diverse set of
relevance signals, but are also much faster. Implementations of our samCNN
(Simple Attention-based Matching CNN) models are shared with the community to
support future work.
