Abstract
We present an incremental dependency parsing model that jointly performs disfluency detection. The model handles speech repairs using a novel non-monotonic transition system, and includes several novel classes of features. For comparison, we evaluated two pipeline systems, using state-of-the-art disfluency detectors. The joint model performed better on both tasks, with a parse accuracy of 90.5% and 84.0% accuracy at disfluency detection. The model runs in expected linear time, and processes over 550 tokens a second.- Anthology ID:
- Q14-1011
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 2
- Month:
- Year:
- 2014
- Address:
- Cambridge, MA
- Editors:
- Dekang Lin, Michael Collins, Lillian Lee
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 131–142
- Language:
- URL:
- https://aclanthology.org/Q14-1011
- DOI:
- 10.1162/tacl_a_00171
- Cite (ACL):
- Matthew Honnibal and Mark Johnson. 2014. Joint Incremental Disfluency Detection and Dependency Parsing. Transactions of the Association for Computational Linguistics, 2:131–142.
- Cite (Informal):
- Joint Incremental Disfluency Detection and Dependency Parsing (Honnibal & Johnson, TACL 2014)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/Q14-1011.pdf
- Data
- Penn Treebank