Michael Schlichtkrull


2023

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Multimodal Automated Fact-Checking: A Survey
Mubashara Akhtar | Michael Schlichtkrull | Zhijiang Guo | Oana Cocarascu | Elena Simperl | Andreas Vlachos
Findings of the Association for Computational Linguistics: EMNLP 2023

Misinformation is often conveyed in multiple modalities, e.g. a miscaptioned image. Multimodal misinformation is perceived as more credible by humans, and spreads faster than its text-only counterparts. While an increasing body of research investigates automated fact-checking (AFC), previous surveys mostly focus on text. In this survey, we conceptualise a framework for AFC including subtasks unique to multimodal misinformation. Furthermore, we discuss related terms used in different communities and map them to our framework. We focus on four modalities prevalent in real-world fact-checking: text, image, audio, and video. We survey benchmarks and models, and discuss limitations and promising directions for future research

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The Intended Uses of Automated Fact-Checking Artefacts: Why, How and Who
Michael Schlichtkrull | Nedjma Ousidhoum | Andreas Vlachos
Findings of the Association for Computational Linguistics: EMNLP 2023

Automated fact-checking is often presented as an epistemic tool that fact-checkers, social media consumers, and other stakeholders can use to fight misinformation. Nevertheless, few papers thoroughly discuss how. We document this by analysing 100 highly-cited papers, and annotating epistemic elements related to intended use, i.e., means, ends, and stakeholders. We find that narratives leaving out some of these aspects are common, that many papers propose inconsistent means and ends, and that the feasibility of suggested strategies rarely has empirical backing. We argue that this vagueness actively hinders the technology from reaching its goals, as it encourages overclaiming, limits criticism, and prevents stakeholder feedback. Accordingly, we provide several recommendations for thinking and writing about the use of fact-checking artefacts.

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Are Embedded Potatoes Still Vegetables? On the Limitations of WordNet Embeddings for Lexical Semantics
Xuyou Cheng | Michael Schlichtkrull | Guy Emerson
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Knowledge Base Embedding (KBE) models have been widely used to encode structured information from knowledge bases, including WordNet. However, the existing literature has predominantly focused on link prediction as the evaluation task, often neglecting exploration of the models’ semantic capabilities. In this paper, we investigate the potential disconnect between the performance of KBE models of WordNet on link prediction and their ability to encode semantic information, highlighting the limitations of current evaluation protocols. Our findings reveal that some top-performing KBE models on the WN18RR benchmark exhibit subpar results on two semantic tasks and two downstream tasks. These results demonstrate the inadequacy of link prediction benchmarks for evaluating the semantic capabilities of KBE models, suggesting the need for a more targeted assessment approach.

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Proceedings of the Sixth Fact Extraction and VERification Workshop (FEVER)
Mubashara Akhtar | Rami Aly | Christos Christodoulopoulos | Oana Cocarascu | Zhijiang Guo | Arpit Mittal | Michael Schlichtkrull | James Thorne | Andreas Vlachos
Proceedings of the Sixth Fact Extraction and VERification Workshop (FEVER)

2022

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A Survey on Automated Fact-Checking
Zhijiang Guo | Michael Schlichtkrull | Andreas Vlachos
Transactions of the Association for Computational Linguistics, Volume 10

Fact-checking has become increasingly important due to the speed with which both information and misinformation can spread in the modern media ecosystem. Therefore, researchers have been exploring how fact-checking can be automated, using techniques based on natural language processing, machine learning, knowledge representation, and databases to automatically predict the veracity of claims. In this paper, we survey automated fact-checking stemming from natural language processing, and discuss its connections to related tasks and disciplines. In this process, we present an overview of existing datasets and models, aiming to unify the various definitions given and identify common concepts. Finally, we highlight challenges for future research.

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UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering
Barlas Oguz | Xilun Chen | Vladimir Karpukhin | Stan Peshterliev | Dmytro Okhonko | Michael Schlichtkrull | Sonal Gupta | Yashar Mehdad | Scott Yih
Findings of the Association for Computational Linguistics: NAACL 2022

We study open-domain question answering with structured, unstructured and semi-structured knowledge sources, including text, tables, lists and knowledge bases. Departing from prior work, we propose a unifying approach that homogenizes all sources by reducing them to text and applies the retriever-reader model which has so far been limited to text sources only. Our approach greatly improves the results on knowledge-base QA tasks by 11 points, compared to latest graph-based methods. More importantly, we demonstrate that our unified knowledge (UniK-QA) model is a simple and yet effective way to combine heterogeneous sources of knowledge, advancing the state-of-the-art results on two popular question answering benchmarks, NaturalQuestions and WebQuestions, by 3.5 and 2.6 points, respectively. The code of UniK-QA is available at: https://github.com/facebookresearch/UniK-QA.

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Proceedings of the Fifth Fact Extraction and VERification Workshop (FEVER)
Rami Aly | Christos Christodoulopoulos | Oana Cocarascu | Zhijiang Guo | Arpit Mittal | Michael Schlichtkrull | James Thorne | Andreas Vlachos
Proceedings of the Fifth Fact Extraction and VERification Workshop (FEVER)

2021

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Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER)
Rami Aly | Christos Christodoulopoulos | Oana Cocarascu | Zhijiang Guo | Arpit Mittal | Michael Schlichtkrull | James Thorne | Andreas Vlachos
Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER)

2017

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Cross-Lingual Dependency Parsing with Late Decoding for Truly Low-Resource Languages
Michael Schlichtkrull | Anders Søgaard
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

In cross-lingual dependency annotation projection, information is often lost during transfer because of early decoding. We present an end-to-end graph-based neural network dependency parser that can be trained to reproduce matrices of edge scores, which can be directly projected across word alignments. We show that our approach to cross-lingual dependency parsing is not only simpler, but also achieves an absolute improvement of 2.25% averaged across 10 languages compared to the previous state of the art.

2016

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MSejrKu at SemEval-2016 Task 14: Taxonomy Enrichment by Evidence Ranking
Michael Schlichtkrull | Héctor Martínez Alonso
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)