Unlocking Transfer Learning in Argumentation Mining: A Domain Independent Modelling Approach
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 laborintensive 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.
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