| Modeling relational data with graph convolutional networks MS Schlichtkrull, TN Kipf, P Bloem, R van den Berg, I Titov, M Welling European Semantic Web Conference, 593-607, 2018 | 5633 | 2018 |
| A survey on automated fact-checking Z Guo, M Schlichtkrull, A Vlachos Transactions of the Association for Computational Linguistics 10, 178-206, 2022 | 418 | 2022 |
| Interpreting graph neural networks for NLP with differentiable edge masking MS Schlichtkrull, N De Cao, I Titov The Ninth International Conference on Learning Representations, 2020 | 246 | 2020 |
| Feverous: Fact extraction and verification over unstructured and structured information R Aly, Z Guo, M Schlichtkrull, J Thorne, A Vlachos, C Christodoulopoulos, ... arXiv preprint arXiv:2106.05707, 2021 | 145 | 2021 |
| Unik-qa: Unified representations of structured and unstructured knowledge for open-domain question answering B Oguz, X Chen, V Karpukhin, S Peshterliev, D Okhonko, M Schlichtkrull, ... arXiv preprint arXiv:2012.14610, 2020 | 103 | 2020 |
| How do decisions emerge across layers in neural models? interpretation with differentiable masking N De Cao, M Schlichtkrull, W Aziz, I Titov arXiv preprint arXiv:2004.14992, 2020 | 83 | 2020 |
| Neurips 2020 efficientqa competition: Systems, analyses and lessons learned S Min, J Boyd-Graber, C Alberti, D Chen, E Choi, M Collins, K Guu, ... NeurIPS 2020 Competition and Demonstration Track, 86-111, 2021 | 73 | 2021 |
| The fact extraction and VERification over unstructured and structured information (FEVEROUS) shared task R Aly, Z Guo, MS Schlichtkrull, J Thorne, A Vlachos, ... Proceedings of the Fourth Workshop on Fact Extraction and VERification …, 2021 | 66 | 2021 |
| Unified open-domain question answering with structured and unstructured knowledge B Oguz, X Chen, V Karpukhin, S Peshterliev, D Okhonko, M Schlichtkrull, ... arXiv preprint arXiv:2012.14610, 2020 | 35 | 2020 |
| AVeriTeC: A dataset for real-world claim verification with evidence from the web M Schlichtkrull, Z Guo, A Vlachos Advances in Neural Information Processing Systems 36, 2023 | 33 | 2023 |
| Joint verification and reranking for open fact checking over tables M Schlichtkrull, V Karpukhin, B Oğuz, M Lewis, W Yih, S Riedel Proceedings of the 59th Annual Meeting of the Association for Computational …, 2020 | 25 | 2020 |
| Cross-lingual dependency parsing with late decoding for truly low-resource languages MS Schlichtkrull, A Søgaard Proceedings of the 15th Conference of the European Chapter of the …, 2017 | 19 | 2017 |
| Msejrku at semeval-2016 task 14: Taxonomy enrichment by evidence ranking M Schlichtkrull, HM Alonso Proceedings of the 10th international workshop on semantic evaluation …, 2016 | 19 | 2016 |
| Multimodal automated fact-checking: A survey M Akhtar, M Schlichtkrull, Z Guo, O Cocarascu, E Simperl, A Vlachos Findings of the Association for Computational Linguistics: EMNLP 2023, 5430–5448, 2023 | 18 | 2023 |
| Learning affective projections for emoticons on Twitter MS Schlichtkrull 2015 6th IEEE International Conference on Cognitive Infocommunications …, 2015 | 12 | 2015 |
| The intended uses of automated fact-checking artefacts: Why, how and who M Schlichtkrull, N Ousidhoum, A Vlachos Findings of the Association for Computational Linguistics: EMNLP 2023, 8618–8642, 2023 | 9 | 2023 |
| Evaluating for diversity in question generation over text MS Schlichtkrull, W Cheng arXiv preprint arXiv:2008.07291, 2020 | 5 | 2020 |
| Automated Focused Feedback Generation for Scientific Writing Assistance E Chamoun, M Schlichktrull, A Vlachos Findings of the Association for Computational Linguistics ACL 2024, 2024 | 4 | 2024 |
| Incorporating structure into neural models for language processing MS Schlichtkrull University of Amsterdam, 2021 | 3 | 2021 |
| Generating Media Background Checks for Automated Source Critical Reasoning M Schlichtkrull arXiv preprint arXiv:2409.00781, 2024 | | 2024 |
