Unpacking Ambiguous Structure: A Dataset for Ambiguous Implicit Discourse Relations for English and Egyptian Arabic

Ahmed Ruby, Sara Stymne, Christian Hardmeier

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review

Abstract

In this paper, we present principles of constructing and resolving ambiguity in implicit discourse relations. Following these principles, we created a dataset in both English and Egyptian Arabic that controls for semantic disambiguation, enabling the investigation of prosodic features in future work. In these datasets, examples are two-part sentences with an implicit discourse relation that can be ambiguously read as either causal or concessive, paired with two different preceding context sentences forcing either the causal or the concessive reading. We also validated both datasets by humans and language models (LMs) to study whether context can help humans or LMs resolve ambiguities of implicit relations and identify the intended relation. As a result, this task posed no difficulty for humans, but proved challenging for BERT/CamelBERT and ELECTRA/AraELECTRA models.
Original languageEnglish
Title of host publicationProceedings of the 4th Workshop on Computational Approaches to Discourse (CODI 2023)
Number of pages18
Place of PublicationCanada
PublisherAssociation for Computational Linguistics
Publication date2023
Pages126-144
ISBN (Electronic) 978-1-959429-89-0
DOIs
Publication statusPublished - 2023

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