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 language | American English |
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| Title of host publication | 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 |
| Publisher | Association for Computational Linguistics |
| Pages | 1-27 |
| Number of pages | 27 |
| ISBN (Electronic) | 9781950737604 |
| DOIs | |
| State | Published - 2020 |
| Event | 2019 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
| Name | 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 |
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Conference
| Conference | 2019 Shared Task on Cross-Framework Meaning Representation Parsing, MRP 2019 at the 23rd Conference for Computational Language Learning, CoNLL 2019 |
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| Country/Territory | China |
| City | Hong Kong |
| Period | 3/11/19 → … |
Bibliographical note
Funding Information:We acknowledge the support of the Czech Ministry of Education, Youth, and Sports, through project CZ.02.1.01/0.0/0.0/16 013/0001781 (EF16 013/0001781); Czech Ministry of Culture, project DG16P02R019, the support for the data and services used by the Research Infrastructure projects LM2015071 and LM2018101, also of the Czech Ministry of Education, Youth, and Sports, and the support of the Grant Agency of the Czech Republic, project No. GA17-07313S. The work on UCCA and TUPA was partially supported by the Israel Science Foundation (grant no. 929/17). We also acknowledge the support of the US National Science Foundation on the Uniform Meaning Representation project via Award No. 1763926. All views expressed in this paper are those of the authors and do not necessarily represent the view of the National Science Foundation.
Funding Information:
Many colleagues have assisted in designing the task and preparing its data and software resources. Emily M. Bender and Dan Flickinger provided a critical review of (a sample of) the DM and EDS graphs. Sebastian Schuster made available a prerelease of the converter from PTB-style constituent trees to (basic) UD 2.x dependency graphs. Dotan Dvir has coordinated the team of UCCA annotators, always ensuring that the corpora were ready in time. Andrey Kutuzov helped with the preparation of morpho-syntactic companion trees for the evaluation data. The task design and implementation has benefited from input by the Steering Committee of the ACL Special Interest Group on Natural Language Learning, notably Xavier Carreras and Julia Hockenmaier, as well as by the CoNLL 2019 Programme Chairs, Mohit Bansal and Aline Villavicencio. We are grateful to the Nordic e-Infrastructure Collaboration for their support to the Nordic Language Processing Laboratory (NLPL), which has provided technical infrastructure for the MRP 2019 task. Also, we thankfully acknowledge the assistance of the Linguistic Data Consortium in distributing the training data for the task to participants at no cost to anyone. We acknowledge the support of the Czech Ministry of Education, Youth, and Sports, through project CZ.02.1.01/0.0/0.0/16 013/0001781 (EF16 013/0001781); Czech Ministry of Culture, project DG16P02R019, the support for the data and services used by the Research Infrastructure projects LM2015071 and LM2018101, also of the Czech Ministry of Education, Youth, and Sports, and the support of the Grant Agency of the Czech Republic, project No. GA17-07313S. The work on UCCA and TUPA was partially supported by the Israel Science Foundation (grant no. 929/17). We also acknowledge the support of the US National Science Foundation on the Uniform Meaning Representation project via Award No. 1763926. All views expressed in this paper are those of the authors and do not necessarily represent the view of the National Science Foundation.
Publisher Copyright:
© 2019 Association for Computational Linguistics