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arXiv · 1906.09821

Classification and Clustering of Arguments with Contextualized Word Embeddings

Nils Reimers·Benjamin Schiller·Tilman Beck·Johannes Daxenberger·Christian Stab·Iryna Gurevych
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Abstract

We experiment with two recent contextualized word embedding methods (ELMo and BERT) in the context of open-domain argument search. For the first time, we show how to leverage the power of contextualized word embeddings to classify and cluster topic-dependent arguments, achieving impressive results on both tasks and across multiple datasets. For argument classification, we improve the state-of-the-art for the UKP Sentential Argument Mining Corpus by 20.8 percentage points and for the IBM Debater - Evidence Sentences dataset by 7.4 percentage points. For the understudied task of argument clustering, we propose a pre-training step which improves by 7.8 percentage points over strong baselines on a novel dataset, and by 12.3 percentage points for the Argument Facet Similarity (AFS) Corpus.

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Nils Reimers, Benjamin Schiller, Tilman Beck, Johannes Daxenberger, Christian Stab, Iryna Gurevych. 2019-06-24. Classification and Clustering of Arguments with Contextualized Word Embeddings. https://arxiv.org/abs/1906.09821

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