Papers by Michiel Van Der Meer

4 papers
HintsOfTruth: A Multimodal Checkworthiness Detection Dataset with Real and Synthetic Claims (2025.acl-long)

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Challenge: Identifying checkworthy claims is the first step, but detection methods struggle with content that is (1) multimodal, (2) from diverse domains, and (3) synthetic.
Approach: They propose a dataset for multimodal checkworthiness detection with 27K real-world and synthetic image/claim pairs.
Outcome: The proposed dataset compares lightweight text-based encoders to multimodal models but only focus on claim-like content.
An Empirical Analysis of Diversity in Argument Summarization (2024.eacl-long)

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Challenge: Current methods for summarizing arguments miss an important aspect of diversity . authors examine three aspects of diversity in argument summarization .
Approach: They propose three aspects of diversity that are important for accommodating multiple perspectives.
Outcome: The proposed models lack the diversity of opinions, sources, and annotators.
Annotator-Centric Active Learning for Subjective NLP Tasks (2024.emnlp-main)

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Challenge: Annotator-centric active learning addresses the high costs of collecting human annotations by strategically annotating the most informative samples.
Approach: They propose annotator-centric active learning which incorporates an annotation strategy following data sampling to approximate the full diversity of human judgments.
Outcome: The proposed approach improves data efficiency and performs well in annotator-centric evaluations.
Facilitating Opinion Diversity through Hybrid NLP Approaches (2024.naacl-srw)

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Challenge: Modern democracies face a critical issue of declining citizen participation in decision-making.
Approach: They propose a three-layered hierarchy for representing perspectives obtained by a mixture of human intelligence and large language models.
Outcome: The proposed approach can extract high-level insights into arguments employed by citizens or motivations underlying their decisions.

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