Please use this identifier to cite or link to this item: doi:10.22028/D291-30985
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Title: On the Need of Cross Validation for Discourse Relation Classification
Author(s): Shi, Wei
Demberg, Vera
Editor(s): Kunnemann, Florian
Iñurrieta, Uxoa
Camilleri, John J.
Coll Ardanuy, Mariona
Language: English
Title: European Chapter of the Association for Computational Linguistics - proceedings of the Student Research Workshop : April 3-7, 2017 : EACL 2017
Startpage: 150
Endpage: 156
Publisher/Platform: ACL
Year of Publication: 2017
Place of publication: Stroudsburg, PA
Title of the Conference: EACL 2017
Place of the conference: Valencia, Spain
Publikation type: Conference Paper
Abstract: The task of implicit discourse relation classification has received increased attention in recent years, including two CoNNL shared tasks on the topic. Existing machine learning models for the task train on sections 2-21 of the PDTB and test on section 23, which includes a total of 761 implicit discourse relations. In this paper, we’d like to make a methodological point, arguing that the standard test set is too small to draw conclusions about whether the inclusion of certain features constitute a genuine improvement, or whether one got lucky with some properties of the test set, and argue for the adoption of cross validation for the discourse relation classification task by the community.
DOI of the first publication: 10.18653/v1/E17-2024
URL of the first publication:
Link to this record: hdl:20.500.11880/29702
ISBN: 978-1-945626-37-1
Date of registration: 23-Sep-2020
Faculty: MI - Fakultät für Mathematik und Informatik
Department: MI - Informatik
Professorship: MI - Prof. Dr. Vera Demberg
Collections:Die Universitätsbibliographie

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