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Classification and Clustering of Arguments with Contextualized Word Embeddings

Reimers, Nils ; Schiller, Benjamin ; Beck, Tilman ; Daxenberger, Johannes ; Stab, Christian ; Gurevych, Iryna (2019):
Classification and Clustering of Arguments with Contextualized Word Embeddings.
In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), pp. 567-578,
ACL, 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), Florence, Italy, 28.07.-02.08.2019, ISBN 9781950737482,
[Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2019
Creators: Reimers, Nils ; Schiller, Benjamin ; Beck, Tilman ; Daxenberger, Johannes ; Stab, Christian ; Gurevych, Iryna
Title: Classification and Clustering of Arguments with Contextualized Word Embeddings
Language: English
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.

Title of Book: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019)
Publisher: ACL
ISBN: 9781950737482
Uncontrolled Keywords: UKP_p_OAM
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Ubiquitous Knowledge Processing
DFG-Graduiertenkollegs
DFG-Graduiertenkollegs > Research Training Group 1994 Adaptive Preparation of Information from Heterogeneous Sources
Event Title: 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019)
Event Location: Florence, Italy
Event Dates: 28.07.-02.08.2019
Date Deposited: 30 Oct 2019 13:55
Official URL: https://www.aclweb.org/anthology/P19-1054
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