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  WambsganssMolyndrisSoellnerUnlockingTransferLearning.pdf (872.4Kb)
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Date
2020
Author
Wambsganss, ThiemoMolyndris, NikolaosSöllner, Matthias
Subject
004  Data processing and computer science  300  Social sciences  ArgumentIdentifikationTransferLernenNatürlichsprachiges System
URI
doi:10.17170/kobra-202010231995

doi:10.30844/wi_2020_c9-wambsganss
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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
In: Gronau, Norbert; Heine, Moreen; Krasnova, Hanna; Pousttchi, Key (Hrsg.):  Entwicklungen, Chancen und Herausforderungen der Digitalisierung. Band 1: Proceedings der 15. Internationalen Tagung Wirtschaftsinformatik 2020. GITO Verlag:  Berlin  2020,  S. 341-356;  ISBN 978-3-95545-335-0
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