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Classification and Clustering of Arguments with Contextualized Word Embeddings

Reimers, Nils and Schiller, Benjamin and Beck, Tilman and Daxenberger, Johannes and Stab, Christian and 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), In: The 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), Florence, Italy, 28.07.2019-02.08.2019, pp. 567-578, [Online-Edition: https://www.aclweb.org/anthology/P19-1054],
[Conference or Workshop Item]

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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.

Item Type: Conference or Workshop Item
Erschienen: 2019
Creators: Reimers, Nils and Schiller, Benjamin and Beck, Tilman and Daxenberger, Johannes and Stab, Christian and 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)
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: The 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019)
Event Location: Florence, Italy
Event Dates: 28.07.2019-02.08.2019
Date Deposited: 27 May 2019 13:47
Official URL: https://www.aclweb.org/anthology/P19-1054
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