Abstract
The 2019 Shared Task at the Conference for Computational Language Learning (CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks. Five distinct approaches to the representation of sentence meaning in the form of directed graphs were represented in the training and evaluation data for the task, packaged in a uniform graph abstraction and serialization. The task received submissions from eighteen teams, of which five do not participate in the official ranking because they arrived after the closing deadline, made use of extra training data, or involved one of the task co-organizers. All technical information regarding the task, including system submissions, official results, and links to supporting resources and software are available from the task web site at: http://mrp.nlpl.eu.
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CITATION STYLE
Oepen, S., Abend, O., Hajič, J., Hershcovich, D., Kuhlmann, M., O’Gorman, T., … Urešová, Z. (2020). MRP 2019: Cross-framework meaning representation parsing. In CoNLL 2019 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the Shared Task on Cross-Framework Meaning Representation Parsing at the 2019 Conference on Natural Language Learning (pp. 1–27). Association for Computational Linguistics. https://doi.org/10.18653/v1/K19-2001
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