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@Select Types{,
Journal = "Band-1",
Title= "Unlocking Transfer Learning in Argumentation Mining: A Domain-Independent Modelling Approach",
Author= "Thiemo Wambsganss, Nikolaos Molyndris, Matthias Söllner",
Doi= "https://doi.org/10.30844/wi_2020_c9-wambsganss",
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.
",
Keywords= "Argumentation Mining, Argument Identification, Transfer Learning, Natural Language Processing",
}
Thiemo Wambsganss, Nikolaos Molyndris, Matthias Söllner: Unlocking Transfer Learning in Argumentation Mining: A Domain-Independent Modelling Approach. Online: https://doi.org/10.30844/wi_2020_c9-wambsganss (Abgerufen 19.04.24)
Open Access
Abstract
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.
Keywords
Schlüsselwörter
Argumentation Mining, Argument Identification, Transfer Learning, Natural Language Processing
References
Referenzen
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