Refining Implicit Argument Annotation for UCCA

Ruixiang Cui and Daniel Hershcovich

The Second International Workshop on Designing Meaning Representations (DMR 2020)
Barcelona, Spain, December 13, 2020

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Abstract

Predicate-argument structure analysis is a central component in meaning representations of text. The fact that some arguments are not explicitly mentioned in a sentence gives rise to ambiguity in language understanding, and renders it difficult for machines to interpret text correctly. However, only few resources represent implicit roles for NLU, and existing studies in NLP only make coarse distinctions between categories of arguments omitted from linguistic form. To better understand the behaviour of implicit roles and their characteristics, in this paper, we design a typology for fine-grained implicit argument annotation on top of Universal Conceptual Cognitive Annotation's foundational layer. The proposed implicit argument categorisation is driven by theories of implicit role interpretation and consists of six types: Deictic, Generic, Genre-based, Type-identifiable, Non-specific, and Iterated-set. We exemplify our design by revisiting part of the UCCA EWT corpus, providing a new dataset annotated with the refinement layer, and making a comparative analysis with other schemes both in terms of quantity and quality.

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