Please use this identifier to cite or link to this item: doi:10.22028/D291-38633
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Title: Comparison of methods for explicit discourse connective identification across various domains
Author(s): Scholman, Merel Cleo Johanna
Dong, Tianai
Yung, Frances Pikyu
Demberg, Vera
Editor(s): Zeldes, Amir
Language: English
Title: The Shared Task on Discourse Relation Parsing and Treebanking - proceedings of the 2nd Shared Task on Discourse Relation Parsing and Treebanking (DISRPT 2021) : November 11, 2021, Punta Cana, Dominican Republic : EMNLP 2021
Pages: 95-106
Publisher/Platform: ACL
Year of Publication: 2021
Place of publication: Stroudsburg, PA
Place of the conference: Punta Cana, Dominican Republic
DDC notations: 004 Computer science, internet
Publikation type: Conference Paper
Abstract: Existing parse methods use varying approaches to identify explicit discourse connectives, but their performance has not been consistently evaluated in comparison to each other, nor have they been evaluated consistently on text other than newspaper articles. We here assess the performance on explicit connective identification of three parse methods (PDTB e2e, Lin et al., 2014; the winner of CONLL2015, Wang et al., 2015; and DisSent, Nie et al., 2019), along with a simple heuristic. We also examine how well these systems generalize to different datasets, namely written newspaper text (PDTB), written scientific text (BioDRB), prepared spoken text (TED-MDB) and spontaneous spoken text (Disco-SPICE). The results show that the e2e parser outperforms the other parse methods in all datasets. However, performance drops significantly from the PDTB to all other datasets. We provide a more fine-grained analysis of domain differences and connectives that prove difficult to parse, in order to highlight the areas where gains can be made.
DOI of the first publication: 10.18653/v1/2021.codi-main.9
Link to this record: urn:nbn:de:bsz:291--ds-386339
ISBN: 978-1-955917-13-1
Date of registration: 2-Jan-2023
Faculty: MI - Fakultät für Mathematik und Informatik
Department: MI - Informatik
Professorship: MI - Prof. Dr. Vera Demberg
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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