Crowdsourcing discourse relation annotations by a two-step connective insertion task

Frances Yung, Merel C.J. Scholman, Vera Demberg

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

Abstract

The perspective of being able to crowd-source coherence relations bears the promise of acquiring annotations for new texts quickly, which could then increase the size and variety of discourse-annotated corpora. It would also open the avenue to answering new research questions: Collecting annotations from a larger number of individuals per instance would allow to investigate the distribution of inferred relations, and to study individual differences in coherence relation interpretation. However, annotating coherence relations with untrained workers is not trivial. We here propose a novel two-step annotation procedure, which extends an earlier method by Scholman and Demberg (2017a). In our approach, coherence relation labels are inferred from connectives that workers insert into the text. We show that the proposed method leads to replicable coherence annotations, and analyse the agreement between the obtained relation labels and annotations from PDTB and RSTDT on the same texts.
Original languageEnglish
Title of host publicationLAW 2019 - 13th Linguistic Annotation Workshop, Proceedings of the Workshop
PublisherAssociation for Computational Linguistics (ACL)
Pages16-25
Number of pages10
ISBN (Print)9781950737383
DOIs
Publication statusPublished - 2019
Externally publishedYes

Publication series

NameLAW 2019 - 13th Linguistic Annotation Workshop, Proceedings of the Workshop

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