Evaluating LSTM models for grammatical function labelling

Bich-Ngoc Do, Ines Rehbein


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
Venues:
IWPT | WS
SIG:
SIGPARSE
Publisher:
Association for Computational Linguistics
Note:
Pages:
128–133
Language:
URL:
https://www.aclweb.org/anthology/W17-6318
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-6318.pdf