The ParisNLP entry at the ConLL UD Shared Task 2017: A Tale of a #ParsingTragedy

Éric de La Clergerie, Benoît Sagot, Djamé Seddah


Abstract
We present the ParisNLP entry at the UD CoNLL 2017 parsing shared task. In addition to the UDpipe models provided, we built our own data-driven tokenization models, sentence segmenter and lexicon-based morphological analyzers. All of these were used with a range of different parsing models (neural or not, feature-rich or not, transition or graph-based, etc.) and the best combination for each language was selected. Unfortunately, a glitch in the shared task’s Matrix led our model selector to run generic, weakly lexicalized models, tailored for surprise languages, instead of our dataset-specific models. Because of this #ParsingTragedy, we officially ranked 27th, whereas our real models finally unofficially ranked 6th.
Anthology ID:
K17-3026
Volume:
Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
Month:
August
Year:
2017
Address:
Vancouver, Canada
Editors:
Jan Hajič, Dan Zeman
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
243–252
Language:
URL:
https://aclanthology.org/K17-3026
DOI:
10.18653/v1/K17-3026
Bibkey:
Cite (ACL):
Éric de La Clergerie, Benoît Sagot, and Djamé Seddah. 2017. The ParisNLP entry at the ConLL UD Shared Task 2017: A Tale of a #ParsingTragedy. In Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, pages 243–252, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
The ParisNLP entry at the ConLL UD Shared Task 2017: A Tale of a #ParsingTragedy (de La Clergerie et al., CoNLL 2017)
Copy Citation:
PDF:
https://aclanthology.org/K17-3026.pdf
Data
Universal Dependencies