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.
|Title of host publication||Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP 2018)|
|Editors||Ellen Riloff, David Chiang, Julia Hockenmaier, Jun'ichi Tsujii|
|Place of Publication||Stroudsburg|
|Publisher||Association for Computational Linguistics (ACL)|
|Number of pages||10|
|Publication status||Published - Nov 2018|
|Event||2018 Conference on Empirical Methods in Natural Language Processing (EMNLP) - Brussels, Belgium|
Duration: 31 Oct 2018 → 4 Nov 2018
|Conference||2018 Conference on Empirical Methods in Natural Language Processing (EMNLP)|
|Period||31/10/18 → 4/11/18|
Bibliographical noteCopyright the Publisher 2018. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.
Jamshid Lou, P., Anderson, P., & Johnson, M. (2018). Disfluency detection using auto-correlational neural networks. In E. Riloff, D. Chiang, J. Hockenmaier, & J. Tsujii (Eds.), Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP 2018) (pp. 4610-4619). [D18-1491] Stroudsburg: Association for Computational Linguistics (ACL).