Michael Schlichtkrull


2022

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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)

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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.

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)