Currently, state-of-the-art approaches to disfluency detection depend heavily on hand-crafted pattern match features, specifically designed to find such "rough copies" (Zayats et al., 2016;Jamshid Lou and Johnson, 2017).
to many other sequence tagging tasks (Plank et al., 2016;Yu et al., 2017), "vanilla" convolutional neural networks (CNNs) and long shortterm memory (LSTM) models operating only on words or characters are surprisingly poor at disfluency detection (Zayats et al., 2016).