# The First Shared Task on Discourse Representation Structure Parsing

arXiv: https://arxiv.org/abs/2005.13399 · HTML: https://mlsanity.com/paper/2005.13399/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2020.

## Built on by (most cited first, 5 of 5)

- [PMB5: Gaining More Insight into Neural Semantic Parsing with Challenging Benchmarks](https://mlsanity.com/paper/2404.08354/) (2024)
- [On Adversarial Robustness of Synthetic Code Generation](https://mlsanity.com/paper/2106.11629/) (2021)
- [Discourse Representation Structure Parsing for Chinese](https://mlsanity.com/paper/2306.09725/) (2023)
- [A Compositional Typed Semantics for Universal Dependencies](https://mlsanity.com/paper/2403.01187/) (2024)
- [CogNarr Ecosystem: Facilitating Group Cognition at Scale](https://mlsanity.com/paper/2407.18945/) (2024)

## Abstract (arXiv)

The paper presents the IWCS 2019 shared task on semantic parsing where the goal is to produce Discourse Representation Structures (DRSs) for English sentences. DRSs originate from Discourse Representation Theory and represent scoped meaning representations that capture the semantics of negation, modals, quantification, and presupposition triggers. Additionally, concepts and event-participants in DRSs are described with WordNet synsets and the thematic roles from VerbNet. To measure similarity between two DRSs, they are represented in a clausal form, i.e. as a set of tuples. Participant systems were expected to produce DRSs in this clausal form. Taking into account the rich lexical information, explicit scope marking, a high number of shared variables among clauses, and highly-constrained format of valid DRSs, all these makes the DRS parsing a challenging NLP task. The results of the shared task displayed improvements over the existing state-of-the-art parser.