  In recent years, the natural language processing community has moved away
from task-specific feature engineering, i.e., researchers discovering ad-hoc
feature representations for various tasks, in favor of general-purpose methods
that learn the input representation by themselves. However, state-of-the-art
approaches to disfluency detection in spontaneous speech transcripts currently
still depend on an array of hand-crafted features, and other representations
derived from the output of pre-existing systems such as language models or
dependency parsers. As an alternative, this paper proposes a simple yet
effective model for automatic disfluency detection, called an
auto-correlational neural network (ACNN). The model uses a convolutional neural
network (CNN) and augments it with a new auto-correlation operator at the
lowest layer that can capture the kinds of "rough copy" dependencies that are
characteristic of repair disfluencies in speech. In experiments, the ACNN model
outperforms the baseline CNN on a disfluency detection task with a 5% increase
in f-score, which is close to the previous best result on this task.
