MRP 2019: Cross-framework meaning representation parsing

Stephan Oepen, Omri Abend, Jan Hajič, Daniel Hershcovich, Marco Kuhlmann, Tim O'Gorman, Nianwen Xue, Jayeol Chun, Milan Straka, Zdeňka Urešová

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageAmerican English
Title of host publicationCoNLL 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
Pages1-27
Number of pages27
ISBN (Electronic)9781950737604
DOIs
StatePublished - 2020
Event2019 Shared Task on Cross-Framework Meaning Representation Parsing, MRP 2019 at the 23rd Conference for Computational Language Learning, CoNLL 2019 - Hong Kong, China
Duration: 3 Nov 2019 → …

Publication series

NameCoNLL 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

Conference

Conference2019 Shared Task on Cross-Framework Meaning Representation Parsing, MRP 2019 at the 23rd Conference for Computational Language Learning, CoNLL 2019
Country/TerritoryChina
CityHong Kong
Period3/11/19 → …

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Linguistics and Language
  • Artificial Intelligence

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