Classification and Clustering of Arguments with Contextualized Word Embeddings
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.
Keywords
Cite
@article{arxiv.1906.09821,
title = {Classification and Clustering of Arguments with Contextualized Word Embeddings},
author = {Nils Reimers and Benjamin Schiller and Tilman Beck and Johannes Daxenberger and Christian Stab and Iryna Gurevych},
journal= {arXiv preprint arXiv:1906.09821},
year = {2019}
}
Comments
Conference paper at ACL 2019