CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

By Daniel Zeman, Martin Popel, Milan Straka, Jan Hajič, Joakim Nivre, Filip Ginter, Juhani Luotolahti, Sampo Pyysalo, Slav Petrov, Martin Potthast and 51 more. Conference on Computational Natural Language Learning, 2017

354 citations recorded by the source corpus.

The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets. In 2017, the task was devoted to learning dependency parsers for a large number of languages, in a real-world setting without any gold-standard annotation on input. All test sets followed a unified annotation scheme, namely that of Universal Dependencies. In this paper, we define the task and evaluation methodology, describe how the data sets were prepared, report and analyze the main results, and provide a brief categorization of the different approaches of the participating systems.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/5c0a7a3f4a7928e8d5fe0b05d73d53865f5afb3d

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In Artificial Intelligence

Citations in the corpus

SparseMAP: Differentiable Sparse Structured Inference (2018, methodology): “We use annotated Universal Dependency data (Nivre et al., 2016), as used in the CoNLL 2017 shared task (Zeman et al., 2017).”
English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too (2020, background): “When there is enough data in multiple languages with consistent annotations, training multilingual models becomes an option for cross-lingual transfer (Plank et al., 2016; Cotterell et al., 2017; Zeman et al., 2017).”
Universal Dependencies (2021, neutral): “…have already had a significant impact on NLP research, most notably for multilingual dependency parsing through two editions of CoNLL shared tasks (Zeman et al. 2017, 2018), which have created a new generation of parsers that handle a large number of languages and that parse from raw text rather…”
From POS tagging to dependency parsing for biomedical event extraction (2018, neutral): “These default values can be considered as optimal ones as they helped producing the highest scores for 57 test sets (including English test sets) and second highest scores for 14 test sets over total 81 test sets across 45 different languages at the CoNLL 2017 shared task [31].”
Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection (2020, background): “0 (2017) are in blue •, and those released in v2.”