| The fact extraction and VERification (FEVER) shared task J Thorne, A Vlachos, O Cocarascu, C Christodoulopoulos, A Mittal arXiv preprint arXiv:1811.10971, 2018 | 251 | 2018 |
| FEVEROUS: Fact extraction and VERification over unstructured and structured information R Aly, Z Guo, M Schlichtkrull, J Thorne, A Vlachos, C Christodoulopoulos, ... NeurIPS Datasets and Benchmarks, 2021 | 179 | 2021 |
| The FEVER2. 0 shared task J Thorne, A Vlachos, O Cocarascu, C Christodoulopoulos, A Mittal Proceedings of the Second Workshop on Fact Extraction and VERification …, 2019 | 126 | 2019 |
| Argumentation-based recommendations: Fantastic explanations and how to find them A Rago, O Cocarascu, F Toni Proceedings of the Twenty-Seventh International Joint Conference on …, 2018 | 97 | 2018 |
| Identifying attack and support argumentative relations using deep learning O Cocarascu, F Toni Proceedings of the 2017 Conference on Empirical Methods in Natural Language …, 2017 | 88 | 2017 |
| 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 | 84 | 2021 |
| Extracting Dialogical Explanations for Review Aggregations with Argumentative Dialogical Agents. O Cocarascu, A Rago, F Toni AAMAS, 1261-1269, 2019 | 73 | 2019 |
| Argumentative explanations for interactive recommendations A Rago, O Cocarascu, C Bechlivanidis, D Lagnado, F Toni Artificial Intelligence 296, 103506, 2021 | 64 | 2021 |
| Argumentation for machine learning: A survey O Cocarascu, F Toni Computational Models of Argument, 219-230, 2016 | 62 | 2016 |
| Data-empowered argumentation for dialectically explainable predictions O Cocarascu, A Stylianou, K Čyras, F Toni ECAI 2020, 2449-2456, 2020 | 52 | 2020 |
| Argumentation as a framework for interactive explanations for recommendations A Rago, O Cocarascu, C Bechlivanidis, F Toni Proceedings of the International Conference on Principles of Knowledge …, 2020 | 49 | 2020 |
| Combining deep learning and argumentative reasoning for the analysis of social media textual content using small data sets O Cocarascu, F Toni Computational Linguistics 44 (4), 833-858, 2018 | 36 | 2018 |
| Detecting deceptive reviews using argumentation O Cocarascu, F Toni Proceedings of the 1st International Workshop on AI for Privacy and Security …, 2016 | 35 | 2016 |
| Multimodal automated fact-checking: A survey M Akhtar, M Schlichtkrull, Z Guo, O Cocarascu, E Simperl, A Vlachos arXiv preprint arXiv:2305.13507, 2023 | 32 | 2023 |
| Dataset independent baselines for relation prediction in argument mining O Cocarascu, E Cabrio, S Villata, F Toni Computational Models of Argument, 45-52, 2020 | 32 | 2020 |
| Explanatory predictions with artificial neural networks and argumentation O Cocarascu, K Cyras, F Toni Workshop on Explainable Artificial Intelligence, 2018 | 29 | 2018 |
| Reading and Reasoning over Chart Images for Evidence-based Automated Fact-Checking M Akhtar, O Cocarascu, E Simperl Findings of the Association for Computational Linguistics: EACL 2023, 2023 | 21 | 2023 |
| PubHealthTab: A public health table-based dataset for evidence-based fact checking M Akhtar, O Cocarascu, E Simperl Findings of the Association for Computational Linguistics: NAACL 2022, 1-16, 2022 | 20 | 2022 |
| Exploring the Numerical Reasoning Capabilities of Language Models: A Comprehensive Analysis on Tabular Data M Akhtar, A Shankarampeta, V Gupta, A Pital, O Cocarascu, E Simperl Findings of the Association for Computational Linguistics: EMNLP 2023, 2023 | 15 | 2023 |
| Mining bipolar argumentation frameworks from natural language text O Cocarascu, F Toni The 17th Workshop on Computational Models of Natural Argument, 2017 | 10 | 2017 |
Francesca ToniImperial College LondonAdresse e-mail validée de imperial.ac.uk
