Papers by Michael Sejr Schlichtkrull
Joint Verification and Reranking for Open Fact Checking Over Tables (2021.acl-long)
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Michael Sejr Schlichtkrull, Vladimir Karpukhin, Barlas Oguz, Mike Lewis, Wen-tau Yih, Sebastian Riedel
| Challenge: | Existing research into structured data has focused on textual data and the closed-domain setting is not reflective of real-world fact checking tasks. |
| Approach: | They propose a joint reranking-and-verification model which fuses evidence documents in the verification component and a heuristic retrieval baseline. |
| Outcome: | The proposed model achieves comparable performance to the closed-domain state-of-the-art on the TabFact dataset and significantly improves over a heuristic retrieval baseline. |
Attacks by Content: Automated Fact-checking is an AI Security Issue (2025.emnlp-main)
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| Challenge: | Existing defenses focus on detecting hidden commands but are ineffective against content attacks. |
| Approach: | They propose to repurpose retrieval-augmented generation (RAG) as a cognitive self-defense tool for agents. |
| Outcome: | The proposed approach is analogous to an existing task, automated fact-checking, and could be used to defend agents against content attacks. |
PledgeTracker: A System for Monitoring the Fulfilment of Pledges (2025.emnlp-demos)
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Yulong Chen, Michael Sejr Schlichtkrull, Zhenyun Deng, David Corney, Nasim Asl, Joshua Salisbury, Andrew Dudfield, Andreas Vlachos
| Challenge: | Existing methods simplify pledge verification into document classification task, overlooking its dynamic temporal and multi-document nature. |
| Approach: | They propose a system that reformulates pledge verification into structured event timeline construction. |
| Outcome: | The proposed system shows that it can be used in real-world workflows and reduces human verification effort. |
IYKYK: Using language models to decode extremist cryptolects (2026.eacl-long)
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Christine de Kock, Arij Riabi, Zeerak Talat, Michael Sejr Schlichtkrull, Pranava Madhyastha, Eduard Hovy
| Challenge: | Extremist groups develop complex in-group language to exclude or mislead outsiders . general purpose LLMs cannot consistently detect or decode extremist language . |
| Approach: | They evaluate the ability of current language technologies to detect and interpret the cryptolects of two online extremist platforms. |
| Outcome: | The proposed models can detect and interpret extremist language better than current models. |
Social Good or Scientific Curiosity? Uncovering the Research Framing Behind NLP Artefacts (2025.emnlp-main)
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| Challenge: | Recent studies show that few papers explicitly identify key stakeholders, intended uses, or appropriate contexts. |
| Approach: | They propose to automate analysis of NLP research by extracting key elements and linking them through interpretable rules and contextual reasoning. |
| Outcome: | The proposed system improves on two domains of fact-checking and hate speech detection. |
How do Decisions Emerge across Layers in Neural Models? Interpretation with Differentiable Masking (2020.emnlp-main)
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| Challenge: | Attribution methods assess the contribution of inputs to the model prediction. |
| Approach: | They propose a method which removes subsets of inputs and a model which is based on hidden layers to make the decision to include or disregard an input token. |
| Outcome: | The proposed method is efficient because it predicts rather than searches the inputs. |