Abstract
To improve grammatical function labelling for German, we augment the labelling component of a neural dependency parser with a decision history. We present different ways to encode the history, using different LSTM architectures, and show that our models yield significant improvements, resulting in a LAS for German that is close to the best result from the SPMRL 2014 shared task (without the reranker).- Anthology ID:
- W17-6318
- Volume:
- Proceedings of the 15th International Conference on Parsing Technologies
- Month:
- September
- Year:
- 2017
- Address:
- Pisa, Italy
- Editors:
- Yusuke Miyao, Kenji Sagae
- Venue:
- IWPT
- SIG:
- SIGPARSE
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 128–133
- Language:
- URL:
- https://aclanthology.org/W17-6318
- DOI:
- Cite (ACL):
- Bich-Ngoc Do and Ines Rehbein. 2017. Evaluating LSTM models for grammatical function labelling. In Proceedings of the 15th International Conference on Parsing Technologies, pages 128–133, Pisa, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Evaluating LSTM models for grammatical function labelling (Do & Rehbein, IWPT 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W17-6318.pdf