Date
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Konferenzveröffentlichung
Unlocking Transfer Learning in Argumentation Mining: A Domain-Independent Modelling Approach
Abstract
Argument identification is the fundamental block of every Argumentation Mining pipeline, which in turn is a young upcoming field with multiple applications ranging from strategy support to opinion mining and news fact-checking. We developed a model, which is tackling the two biggest practical and academic challenges of the research field today. First, it addresses the lack of corpus-agnostic models and, second, it tackles the problem of human-labor-intensive NLP models being costly to develop. We do that by suggesting and implementing an easy-to-use solution that utilizes the latest advancements in natural language Transfer Learning. The result is a two-fold contribution: A system that delivers state-of-the-art results in multiple corpora and opens up a new way of academic advancement of the field through Transfer Learning. Additionally, it provides the architecture for an easy-to-use tool that can be used for practical applications without the need for domain-specific knowledge.
Citation
@inproceedings{doi:10.17170/kobra-202010231995,
author={Wambsganss, Thiemo and Molyndris, Nikolaos and Söllner, Matthias},
title={Unlocking Transfer Learning in Argumentation Mining: A Domain-Independent Modelling Approach},
booktitle={Entwicklungen, Chancen und Herausforderungen der Digitalisierung. Band 1: Proceedings der 15. Internationalen Tagung Wirtschaftsinformatik 2020},
year={2020}
}
0500 Oax
0501 Text $btxt$2rdacontent
0502 Computermedien $bc$2rdacarrier
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1500 1/eng
2050 ##0##http://hdl.handle.net/123456789/11899
3000 Wambsganss, Thiemo
3010 Molyndris, Nikolaos
3010 Söllner, Matthias
4000 Unlocking Transfer Learning in Argumentation Mining: A Domain-Independent Modelling Approach / Wambsganss, Thiemo
4030
4060 Online-Ressource
4085 ##0##=u http://nbn-resolving.de/http://hdl.handle.net/123456789/11899=x R
4204 \$dKonferenzveröffentlichung
4170
5550 {{Argument}}
5550 {{Identifikation}}
5550 {{Transfer}}
5550 {{Lernen}}
5550 {{Natürlichsprachiges System}}
7136 ##0##http://hdl.handle.net/123456789/11899
<resource xsi:schemaLocation="http://datacite.org/schema/kernel-2.2 http://schema.datacite.org/meta/kernel-2.2/metadata.xsd"> 2020-10-26T14:36:13Z 2020-10-26T14:36:13Z 2020 doi:10.17170/kobra-202010231995 http://hdl.handle.net/123456789/11899 eng GITO Verlag Urheberrechtlich geschützt https://rightsstatements.org/page/InC/1.0/ argumentation mining argument identification transfer learning natural language processing 004 300 Unlocking Transfer Learning in Argumentation Mining: A Domain-Independent Modelling Approach Konferenzveröffentlichung Argument identification is the fundamental block of every Argumentation Mining pipeline, which in turn is a young upcoming field with multiple applications ranging from strategy support to opinion mining and news fact-checking. We developed a model, which is tackling the two biggest practical and academic challenges of the research field today. First, it addresses the lack of corpus-agnostic models and, second, it tackles the problem of human-labor-intensive NLP models being costly to develop. We do that by suggesting and implementing an easy-to-use solution that utilizes the latest advancements in natural language Transfer Learning. The result is a two-fold contribution: A system that delivers state-of-the-art results in multiple corpora and opens up a new way of academic advancement of the field through Transfer Learning. Additionally, it provides the architecture for an easy-to-use tool that can be used for practical applications without the need for domain-specific knowledge. open access Wambsganss, Thiemo Molyndris, Nikolaos Söllner, Matthias Berlin doi:10.30844/wi_2020_c9-wambsganss Argument Identifikation Transfer Lernen Natürlichsprachiges System publishedVersion 2020-03 Potsdam Entwicklungen, Chancen und Herausforderungen der Digitalisierung. Band 1: Proceedings der 15. Internationalen Tagung Wirtschaftsinformatik 2020 Gronau, Norbert Heine, Moreen Krasnova, Hanna Pousttchi, Key ISBN 978-3-95545-335-0 341-356 false 15. Internationalen Tagung Wirtschaftsinformatik 2020 </resource>
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