Disfluency Detection using Auto-Correlational Neural Networks
This addresses the problem of disfluency detection for natural language processing applications, but it is incremental as it builds on existing neural methods with a specific enhancement.
The paper tackles disfluency detection in spontaneous speech transcripts by proposing an auto-correlational neural network (ACNN), which outperforms a baseline CNN with a 5% increase in f-score, nearing previous best results.
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.