  In this paper we introduce a novel pattern match neural network architecture
that uses neighbor similarity scores as features, eliminating the need for
feature engineering in a disfluency detection task. We evaluate the approach in
disfluency detection for four different speech genres, showing that the
approach is as effective as hand-engineered pattern match features when used on
in-domain data and achieves superior performance in cross-domain scenarios.
