A non-projective greedy dependency parser with bidirectional LSTMs
Paper · 2017 · David Vilares and Carlos Gómez-Rodríguez
What it is about
BIST-COVINGTON is a greedy parser that implements Covington's algorithm for unrestricted non-projective dependency parsing as a transition system, with O(n^2) complexity. The scorer is a bidirectional LSTM in the style of Kiperwasser and Goldberg, trained with a dynamic oracle to reduce error propagation. The submission used the official UDPipe 1.1 segmentation and tagging, no ensemble and no custom tokenization. It ranked 7th of 33 teams on the CoNLL 2017 big-treebank category (55 treebanks), and 16th and 12th in LAS and UAS over all treebanks. The authors trace their weaker "all" results to four parallel PUD treebanks, where suffixed treebank models transfer poorly.
For Mova
- I1. en/src/parse.rs: add Covington's non-projective transition system as a second decoder over the same features; measure LAS and UAS on EWT test and on GUM test (test only), and the share of gold non-projective arcs that are recovered.
- I2. gparse/src/eisner.rs: measure how many gold EWT test arcs are non-projective and therefore cannot be produced by the Eisner decoder; this sets whether a non-projective decoder is worth the run in I1.
- I3. Runtime of the two decoders on EWT test per 1,000 sentences, to check the O(n^2) claim of the paper against the beam-8 arc-standard search in
en.
Non-projective arcs are the displacement phenomenon of 29-ellipsis-word-order-information-structure and of extraposed clauses in 25-relative-clauses, and the general syntax is in 20-syntax-overview. The code touched is en/src/parse.rs and gparse/src/eisner.rs. Measure first: the non-projective share and the Eisner ceiling of idea I2, since they decide whether idea I1 is worth a run.
Institutions
SpainFields, topics and concepts
📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 9,085 worksArtificial Intelligence · 8,888 worksArts and Humanities · 9,296 worksLanguage and Linguistics · 9,206 works
🧭 Topics 2Research subjects and languages the work is about.English · 1,373 worksDependency parsing · 120 works
Cite
Cite as David Vilares and Carlos Gómez-Rodríguez. 2017. A non-projective greedy dependency parser with bidirectional LSTMs. Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies.
@inproceedings{vilares2017non,
title = {{A non-projective greedy dependency parser with bidirectional LSTMs}},
author = {David Vilares and Carlos Gómez-Rodríguez},
booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies},
year = {2017},
doi = {10.18653/v1/k17-3016},
eprint = {1707.03228},
archivePrefix = {arXiv},
url = {https://doi.org/10.18653/v1/k17-3016}
}References
- Željko Agić and Natalie Schluter. 2017. How (not) to train a dependency parser: The curious case of jackknifing part-of-speech taggers. In The 54th Annual Meeting of the Association for Computational Linguistics (ACL 2017). find
- Giuseppe Attardi. 2006. Experiments with a mul-tilanguage non-projective dependency parser. In Proceedings of the Tenth Conference on Computational Natural Language Learning. Association for Computational Linguistics, pages 166–170. http://dl.acm.org/citation.cfm?id=1596276.1596307. ↗
- Danqi Chen and Christopher Manning. 2014. A fast and accurate dependency parser using neural networks. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha, Qatar, pages 740–750. http://www.aclweb.org/anthology/D14-1082. \#\# Conclusion ↗
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