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.

本研究使用 ELMo 和 BERT 作为最新的上下文化词嵌入方法，在开放领域的论据搜索中进行了实验。我们首次展示了如何利用上下文化词嵌入的能力，对主题相关的论据进行分类和聚类，并在多个数据集和任务中取得了令人印象深刻的结果。