mlsanityData to 14 Oct 2026

Diversity Over Size: On the Effect of Sample and Topic Sizes for Topic-Dependent Argument Mining Datasets

arXiv 2205.11472 ↗First posted 23 May 2022cs.CLcited by 4

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Abstract (arXiv)

The task of Argument Mining, that is extracting and classifying argument components for a specific topic from large document sources, is an inherently difficult task for machine learning models and humans alike, as large Argument Mining datasets are rare and recognition of argument components requires expert knowledge. The task becomes even more difficult if it also involves stance detection of retrieved arguments. In this work, we investigate the effect of Argument Mining dataset composition in few- and zero-shot settings. Our findings show that, while fine-tuning is mandatory to achieve acceptable model performance, using carefully composed training samples and reducing the training sample size by up to almost 90% can still yield 95% of the maximum performance. This gain is consistent across three Argument Mining tasks on three different datasets. We also publish a new dataset for future benchmarking.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 30 of its 58 references that are in the map. First posted on arXiv 23 May 2022.

Builds on

30 of 58 references.

References · 30

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18 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Large Language Models in Argument Mining: A Surveycited by 14
  2. 2025We Need to Measure Data Diversity in NLP -- Better and Broadercited by 2
  3. 2026emb-diversity: A Tool for Embedding-Based Measurement of Data Diversitycited by 1
  4. 2026Extracting Arguments, Not Just Classifying Them: Instruction-Tuned LLMs for Generative Component Detectioncited by 0