Parser Training with Heterogeneous Treebanks

Sara Stymne, Miryam de Lhoneux, Aaron Smith, Joakim Nivre


Abstract
How to make the most of multiple heterogeneous treebanks when training a monolingual dependency parser is an open question. We start by investigating previously suggested, but little evaluated, strategies for exploiting multiple treebanks based on concatenating training sets, with or without fine-tuning. We go on to propose a new method based on treebank embeddings. We perform experiments for several languages and show that in many cases fine-tuning and treebank embeddings lead to substantial improvements over single treebanks or concatenation, with average gains of 2.0–3.5 LAS points. We argue that treebank embeddings should be preferred due to their conceptual simplicity, flexibility and extensibility.
Anthology ID:
P18-2098
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
619–625
Language:
URL:
https://aclanthology.org/P18-2098
DOI:
10.18653/v1/P18-2098
Bibkey:
Cite (ACL):
Sara Stymne, Miryam de Lhoneux, Aaron Smith, and Joakim Nivre. 2018. Parser Training with Heterogeneous Treebanks. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 619–625, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Parser Training with Heterogeneous Treebanks (Stymne et al., ACL 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/P18-2098.pdf
Poster:
 P18-2098.Poster.pdf
Code
 UppsalaNLP/uuparser