Abstract
We recast dependency parsing as a sequence labeling problem, exploring several encodings of dependency trees as labels. While dependency parsing by means of sequence labeling had been attempted in existing work, results suggested that the technique was impractical. We show instead that with a conventional BILSTM-based model it is possible to obtain fast and accurate parsers. These parsers are conceptually simple, not needing traditional parsing algorithms or auxiliary structures. However, experiments on the PTB and a sample of UD treebanks show that they provide a good speed-accuracy tradeoff, with results competitive with more complex approaches.- Anthology ID:
- N19-1077
- Volume:
- Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 717–723
- Language:
- URL:
- https://aclanthology.org/N19-1077
- DOI:
- 10.18653/v1/N19-1077
- Cite (ACL):
- Michalina Strzyz, David Vilares, and Carlos Gómez-Rodríguez. 2019. Viable Dependency Parsing as Sequence Labeling. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 717–723, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- Viable Dependency Parsing as Sequence Labeling (Strzyz et al., NAACL 2019)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/N19-1077.pdf
- Code
- mstrise/dep2label
- Data
- Penn Treebank