Abstract
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.
Highlights
Semantic parsing has been gaining in popularity in the last few years
There have been a series of shared tasks in semantic parsing organized, where each task requires to generate meaning representations of specific types: Broad-Coverage Broad-coverage Semantic Dependencies (Oepen et al, 2014, 2015), Abstract Meaning Representation (May, 2016; May and Priyadarshi, 2017), or Universal Conceptual Cognitive Annotation (Hershcovich et al, 2019)
The Discourse Representation Structure (DRS) parsing task extends this development by aiming at producing meaning representations that (i) come with more expressive power than existing ones and (ii) are translatable into formal logic, thereby opening the door to applications that require automated forms of inference (Blackburn and Bos, 2005; Dagan et al, 2013)
Summary
Semantic parsing has been gaining in popularity in the last few years. There have been a series of shared tasks in semantic parsing organized, where each task requires to generate meaning representations of specific types: Broad-Coverage Broad-coverage Semantic Dependencies (Oepen et al, 2014, 2015), Abstract Meaning Representation (May, 2016; May and Priyadarshi, 2017), or Universal Conceptual Cognitive Annotation (Hershcovich et al, 2019). The Discourse Representation Structure (DRS) parsing task extends this development by aiming at producing meaning representations that (i) come with more expressive power than existing ones and (ii) are translatable into formal logic, thereby opening the door to applications that require automated forms of inference (Blackburn and Bos, 2005; Dagan et al, 2013). In the first shared task on DRS parsing, taking into account the information-rich and complex structure of the target meaning representation, we tested participant systems mainly on short, open-domain English texts. In this way, we lowered the threshold for participation to encourage higher results in the shared task and mitigate challenges associated to semantic parsing long texts.
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