The 2020 Shared Task at the Conference for Computational Language Learning (CoNLL) was devoted to Meaning Representation Parsing (MRP) across frameworks and languages. Extending a similar setup from the previous year, five distinct approaches to the representation of sentence meaning in the form of directed graphs were represented in the English training and evaluation data for the task, packaged in a uniform graph abstraction and serialization; for four of these representation frameworks, additional training and evaluation data was provided for one additional language per framework. The task received submissions from eight teams, of which two do not participate in the official ranking because they arrived after the closing deadline or made use of additional training data. 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|
|Title of host publication||CoNLL 2020 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2020 Shared Task|
|Subtitle of host publication||Cross-Framework Meaning Representation Parsing|
|Editors||Stephan Oepen, Omri Abend, Lasha Abzianidze, Johan Bos, Jan Hajic, Daniel Hershcovich, Bin Li, Tim O'Gorman, Nianwen Xue, Daniel Zeman|
|Publisher||Association for Computational Linguistics (ACL)|
|Number of pages||22|
|State||Published - 2020|
|Event||Shared Task: Cross-Framework Meaning Representation Parsing at 24th Conference on Computational Natural Language Learning, CoNLL 2020 - Virtual, Online|
Duration: 19 Nov 2020 → 20 Nov 2020
|Name||CoNLL 2020 - SIGNLL Conference on Computational Natural Language Learning, Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing|
|Conference||Shared Task: Cross-Framework Meaning Representation Parsing at 24th Conference on Computational Natural Language Learning, CoNLL 2020|
|Period||19/11/20 → 20/11/20|
Bibliographical noteFunding Information:
Several colleagues have assisted in designing the task and preparing its data and software resources. We thank Dotan Dvir (Hebrew University of Jerusalem) for leading the annotation efforts on UCCA. Dan Flickinger (Stanford University) created fresh gold-standard annotations of some 1, 000 WSJ strings, which form part of the EDS evaluation graphs in 2020. Sebastian Schuster (Stanford University) advised on how to convert the goldstandard syntactic annotations from the venerable PTB and OntoNotes treebanks to Universal Dependencies, version 2.x, using ‘modern’ tokenization. Anna Nedoluzhko and Jiří Mírovský (Charles University in Prague) enhanced the PTG annotation of LPPS data with previously missing items, most notably coreference. Milan Straka (Charles University in Prague) made available an enhanced version of his UDPipe parser and assisted in training Czech, English, and German morpho-syntacic parsing models (for the MRP companion trees). Jayeol Chun (Brandeis University) provided invaluable assistance in conversion of the Chinese AMR annotations, preparation of the Chinese morpho-syntactic companion trees, and provisioning of companion alignments for the English AMR graphs. We are grateful to the Nordic Language Processing Laboratory (NLPL) and Uninett Sigma2, which provided technical infrastructure for the MRP 2020 task. Also, we warmly acknowledge the assistance of the Linguistic Data Consortium (LDC) in distributing the training data for the task to participants at no cost to anyone. The work on UCCA and the HUJI-KU submission was partially supported by the Israel Science Foundation (grant No. 929/17). The work on PTG has been partially supported by the Ministry of Education, Youth and Sports of the Czech Republic (project LINDAT/CLARIAH-CZ, grant No. LM2018101) and partially by the Grant Agency of the Czech Republic (project LUSyD, grant No. GX20-16819X). The work on DRG was supported by the NWO-VICI grant (288-89-003) and the European Union Horizon 2020 research and innovation programme (under grant agreement No. 742204). The work on Chinese AMR data is partially supported by project 18BYY127 under the National Social Science Foundation of China and project 61772278 under the National Science Foundation of China.
© 2020 Association for Computational Linguistics.