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

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.
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https://hal.inria.fr/hal-01584168
Contributor : Benoît Sagot <>
Submitted on : Friday, September 8, 2017 - 2:18:24 PM
Last modification on : Wednesday, December 9, 2020 - 3:45:59 AM

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Éric Villemonte de la Clergerie, Benoît Sagot, Djamé Seddah. The ParisNLP entry at the ConLL UD Shared Task 2017: A Tale of a #ParsingTragedy. Conference on Computational Natural Language Learning, Aug 2017, Vancouver, Canada. pp.243-252, ⟨10.18653/v1/K17-3026⟩. ⟨hal-01584168⟩

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