Work

Classification and Clustering of Arguments with Contextualized Word Embeddings

Year: 2019

Type: conference-paper

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. 1

Source: Annual Meeting of the Association for Computational Linguistics (ACL)

DOI: 10.18653/v1/p19-1054

Authors: Nils Reimers, Benjamin Schiller, Tilman Beck, Johannes Daxenberger, Christian Stab, Iryna Gurevych

Institutions: Technische Universität Darmstadt, Technische Universität Dresden, Christian-Albrechts-Universität zu Kiel

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung

Awards: G5109480000, G5704699130, G7805275943, G8837226822