Please use this identifier to cite or link to this item: doi:10.22028/D291-30471
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Title: Acquiring Annotated Data with Cross-lingual Explicitation for Implicit Discourse Relation Classification
Author(s): Shi, Wei
Yung, Frances Pikyu
Demberg, Vera
Editor(s): Zeldes, Amir
Language: English
Title: The Workshop on Discourse Relation Parsing and Treebanking - proceedings of the workshop : June 6, 2019, Minneapolis, MN : NAACL HLT 2019
Startpage: 12
Endpage: 21
Publisher/Platform: ACL
Year of Publication: 2019
Place of publication: Stroudsburg, PA
Title of the Conference: NAACL-HLT 2019
Place of the conference: Minneapolis, Minnesota, USA
Publikation type: Conference Paper
Abstract: Implicit discourse relation classification is one of the most challenging and important tasks in discourse parsing, due to the lack of connectives as strong linguistic cues. A principle bottleneck to further improvement is the shortage of training data (ca. 18k instances in the Penn Discourse Treebank (PDTB)). Shi et al. (2017) proposed to acquire additional data by exploiting connectives in translation: human translators mark discourse relations which are implicit in the source language explicitly in the translation. Using back-translations of such explicitated connectives improves discourse relation parsing performance. This paper addresses the open question of whether the choice of the translation language matters, and whether multiple translations into different languages can be effectively used to improve the quality of the additional data.
DOI of the first publication: 10.18653/v1/W19-2703
URL of the first publication:
Link to this record: hdl:20.500.11880/28862
ISBN: 978-1-948087-98-8
Date of registration: 12-Mar-2020
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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