---
title: "MRP 2020 — paper by Oepen et al. (2020) — summary and ideas"
description: "MRP 2020 was the second CoNLL shared task on parsing sentences into graph-based meaning… 96 references, cited by 53 papers here."
authors: ["Stephan Oepen", "Omri Abend", "Lasha Abzianidze", "Johan Bos", "Jan Hajic", "Daniel Hershcovich", "Bin Li", "Tim O'Gorman", "Nianwen Xue", "Daniel Zeman"]
year: 2020
venue: "Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing"
source: "https://aclanthology.org/2020.conll-shared.1/"
doi: "10.18653/v1/2020.conll-shared.1"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/oepen-2020-mrp-2020-second-shared-task/"
---
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# MRP 2020: The Second Shared Task on Cross-Framework and Cross-Lingual Meaning Representation Parsing

**Authors:** [Stephan Oepen](https://md.research.elamur.ai/free/en/authors/stephan-oepen/), [Omri Abend](https://md.research.elamur.ai/free/en/authors/omri-abend/), [Lasha Abzianidze](https://md.research.elamur.ai/free/en/authors/lasha-abzianidze/), [Johan Bos](https://md.research.elamur.ai/free/en/authors/johan-bos/), [Jan Hajic](https://md.research.elamur.ai/free/en/authors/jan-hajic/), [Daniel Hershcovich](https://md.research.elamur.ai/free/en/authors/daniel-hershcovich/), [Bin Li](https://md.research.elamur.ai/free/en/authors/bin-li/), [Tim O'Gorman](https://md.research.elamur.ai/free/en/authors/tim-o-gorman/), [Nianwen Xue](https://md.research.elamur.ai/free/en/authors/nianwen-xue/) and [Daniel Zeman](https://md.research.elamur.ai/free/en/authors/daniel-zeman/) · **Year:** 2020 · **Venue:** [Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing](https://md.research.elamur.ai/free/en/venues/conll/) ([CoNLL 2020](https://md.research.elamur.ai/free/en/venues/conll/2020/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2020.conll-shared.1/) · [PDF](https://aclanthology.org/2020.conll-shared.1.pdf) · [DOI 10.18653/v1/2020.conll-shared.1](https://doi.org/10.18653/v1/2020.conll-shared.1) · [OpenAlex](https://openalex.org/W3103028291)

**Citations:** 48 citations (OpenAlex, 2026-10-14) · [53](https://md.research.elamur.ai/free/en/papers/oepen-2020-mrp-2020-second-shared-task/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
MRP 2020 was the second CoNLL shared task on parsing sentences into graph-based meaning representations. It aimed to reduce the fragmentation of the field, where each semantic framework tends to have its own parsers, formats and metrics. The organizers provided English training and test data for five frameworks: EDS, PTG, UCCA, AMR and the newly added DRG, a graph encoding of Discourse Representation Structures. All five use one shared graph abstraction, serialization format and scoring procedure. A separate cross-lingual track added one more language for four frameworks, covering Czech, German and Chinese. The overview also sets out common terminology for semantic graphs and classifies them into three "flavors" by how closely nodes are anchored to the input string. Eight teams submitted systems, and six were officially ranked. Participation fell sharply compared with 2019, but the organizers argue the results are a strong state-of-the-art benchmark, with English AMR as a possible exception. Shared data, companion syntactic trees and evaluation tools make the task a reusable resource for comparing linguistic design choices across frameworks.

## Key points
- Five frameworks were included: EDS, PTG, UCCA, AMR and DRG. DRG is new in 2020, and PTG was expanded to keep more of the original annotation. The bi-lexical DM and PSD graphs from 2019 were dropped to make entry easier.
- The graph-flavor hierarchy distinguishes three levels of anchoring. Flavor (0) covers bi-lexical, fully token-anchored graphs. Flavor (1) allows flexible or partial anchoring. Flavor (2) covers wholly unanchored graphs such as AMR.
- Of eight submitting teams, two were left out of the official ranking for late submission or use of extra training data. Only four of the sixteen MRP 2019 teams took part again.
- Supporting resources included companion morpho-syntactic trees in Universal Dependencies 2.x, built with an enhanced UDPipe for Czech, English and German. They also included converted Chinese AMR data and roughly 1,000 newly annotated WSJ sentences for EDS evaluation. LDC distributed the training data at no cost.
- Parallel annotations over WSJ (Penn Treebank) and LPPS texts allow side-by-side comparison of frameworks. All data, submissions and results are available at http://mrp.nlpl.eu.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Stephan Oepen, Omri Abend, Lasha Abzianidze, Johan Bos, Jan Hajic, Daniel Hershcovich, Bin Li, Tim O'Gorman, Nianwen Xue and Daniel Zeman. 2020. MRP 2020: The Second Shared Task on Cross-Framework and Cross-Lingual Meaning Representation Parsing. Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{oepen2020mrp,
  title = {{MRP 2020: The Second Shared Task on Cross-Framework and Cross-Lingual Meaning Representation Parsing}},
  author = {Stephan Oepen, Omri Abend, Lasha Abzianidze, Johan Bos, Jan Hajic, Daniel Hershcovich, Bin Li, Tim O'Gorman, Nianwen Xue and Daniel Zeman},
  booktitle = {Proceedings of the CoNLL 2020 Shared Task: Cross-Framework Meaning Representation Parsing},
  year = {2020},
  doi = {10.18653/v1/2020.conll-shared.1},
  url = {https://doi.org/10.18653/v1/2020.conll-shared.1}
}
```

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## References

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*Updated 24 October 2026*