A qualitative comparison method for rhetorical structures: identifying different discourse structures in multilingual corpora

Iruskieta, Mikel; Cunha, Iria da; Taboada, Maïté

doi:10.1007/s10579-014-9271-6

Cited by 41 publications

(44 citation statements)

References 34 publications

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“…The inter-annotator agreement is 81.67% for the identification of the CDU (Iruskieta et al, 2015), and 61.47% for the identification of the relations.…”

Section: Rst Corporamentioning

confidence: 91%

Cross-lingual RST Discourse Parsing

Braud¹,

Coavoux²,

Søgaard³

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

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Discourse parsing is an integral part of understanding information flow and argumentative structure in documents. Most previous research has focused on inducing and evaluating models from the English RST Discourse Treebank. However, discourse treebanks for other languages exist, including Spanish, German, Basque, Dutch and Brazilian Portuguese. The treebanks share the same underlying linguistic theory, but differ slightly in the way documents are annotated. In this paper, we present (a) a new discourse parser which is simpler, yet competitive (significantly better on 2/3 metrics) to state of the art for English, (b) a harmonization of discourse treebanks across languages, enabling us to present (c) what to the best of our knowledge are the first experiments on crosslingual discourse parsing.

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“…The inter-annotator agreement is 81.67% for the identification of the CDU (Iruskieta et al, 2015), and 61.47% for the identification of the relations.…”

Section: Rst Corporamentioning

confidence: 91%

Cross-lingual RST Discourse Parsing

Braud¹,

Coavoux²,

Søgaard³

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

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“…In order to check the quality of the annotation process, inter-annotator agreement was measured. This was calculated manually following the qualitative evaluation method (Iruskieta et al, 2015a) using F-measure. In this measurement, in contrast with the automatic tool, the central subconstituent factor was not taken into account.…”

Section: Methodological Stepsmentioning

confidence: 99%

Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019

2019

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This overview summarizes the main contributions of the accepted papers at the 2019 workshop on Discourse Relation Parsing and Treebanking (DISRPT 2019). Co-located with NAACL 2019 in Minneapolis, the workshop's aim was to bring together researchers working on corpus-based and computational approaches to discourse relations. In addition to an invited talk, eighteen papers outlined below were presented, four of which were submitted as part of a shared task on elementary discourse unit segmentation and connective detection.

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“…Wang, Kutschbach, Lüdeling and Stede (Wang et al, 2019) present RST-Tace, a tool for automatic comparison and evaluation of RST trees. RST-Tace serves as an implementation of Iruskieta's comparison method (Iruskieta et al, 2015), which allows trees to be compared and evaluated without the influence of decisions at lower levels in a tree in terms of four factors: constituent, attachment point, nuclearity and relation. RST-Tace can be used regardless of the language or the size of rhetorical trees.…”

Section: Workhop Papersmentioning

confidence: 99%