Classification and Clustering of Arguments with Contextualized Word Embeddings
Open accessAbstract
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
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Simple unsupervised methods as well as state-of-the-art STS systems perform badly on the AFS dataset.
“Hence, simple unsupervised methods as well as state-of-the-art STS systems perform badly on this dataset (Reimers et al., 2019).”
From Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks (2019) · cites this paper as Reimers et al., 2019
Table 3 (section 5.2 Experimental Setup; page unavailable) shows that the tested unsupervised methods had lower correlations on AFS than the supervised methods and the reported human benchmark. The paper tests embeddings including InferSent and BERT, but the cited passages do not establish that it evaluated state-of-the-art STS systems as such, so that part of the claim is not supported. (LLM, conf=0.83)
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Natural Language Decompositions of Implicit Content Enable Better Text Representations
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Frame-oriented Summarization of Argumentative Discussions
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- Authors
- Reimers, N., Schiller, B., Beck, T., Daxenberger, J., Stab, C., & Gurevych, I.
- Affiliations
- Technische Universität Darmstadt (DE)
- Venue
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Published
- 2019
- Pages
- 567-578
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2951281253
- Cited by
- 157
- References
- 22
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 18 · 2023: 30 · 2022: 22 · 2021: 38 · 2020: 29 · 2019: 7
- FWCI
- 9.18
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- TUbilio (Technical University of Darmstadt) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- TUbilio (Technical University of Darmstadt) (submittedVersion)
- TUbilio (Technical University of Darmstadt) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Funders
- Deutsche Forschungsgemeinschaft (SPP 1999, GU 798/25-1, DA 1600/1-1, GU 798/17-1)
- Bundesministerium für Bildung und Forschung
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Arts and Humanities › Philosophy › Linguistics and Discourse Analysis
- Keywords
- argument mining, argument classification, contextual word embeddings, ELMo, BERT, pre-training
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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