| Convolutional 2d knowledge graph embeddings T Dettmers, P Minervini, P Stenetorp, S Riedel Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 3143 | 2018 |
| BRAT: a web-based tool for NLP-assisted text annotation P Stenetorp, S Pyysalo, G Topić, T Ohta, S Ananiadou, J Tsujii Proceedings of the Demonstrations at the 13th Conference of the European …, 2012 | 1663 | 2012 |
| Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity Y Lu, M Bartolo, A Moore, S Riedel, P Stenetorp arXiv preprint arXiv:2104.08786, 2021 | 1022 | 2021 |
| Constructing datasets for multi-hop reading comprehension across documents J Welbl, P Stenetorp, S Riedel Transactions of the Association for Computational Linguistics 6, 287-302, 2018 | 576 | 2018 |
| Dynabench: Rethinking benchmarking in NLP D Kiela, M Bartolo, Y Nie, D Kaushik, A Geiger, Z Wu, B Vidgen, G Prasad, ... arXiv preprint arXiv:2104.14337, 2021 | 410 | 2021 |
| PAQ: 65 million probably-asked questions and what you can do with them P Lewis, Y Wu, L Liu, P Minervini, H Küttler, A Piktus, P Stenetorp, ... Transactions of the Association for Computational Linguistics 9, 1098-1115, 2021 | 208 | 2021 |
| Question and answer test-train overlap in open-domain question answering datasets P Lewis, P Stenetorp, S Riedel arXiv preprint arXiv:2008.02637, 2020 | 204 | 2020 |
| Beat the AI: Investigating adversarial human annotation for reading comprehension M Bartolo, A Roberts, J Welbl, S Riedel, P Stenetorp Transactions of the Association for Computational Linguistics 8, 662-678, 2020 | 177 | 2020 |
| Neural architectures for fine-grained entity type classification S Shimaoka, P Stenetorp, K Inui, S Riedel arXiv preprint arXiv:1606.01341, 2016 | 151 | 2016 |
| What the daam: Interpreting stable diffusion using cross attention R Tang, L Liu, A Pandey, Z Jiang, G Yang, K Kumar, P Stenetorp, J Lin, ... arXiv preprint arXiv:2210.04885, 2022 | 133 | 2022 |
| Ucl machine reading group: Four factor framework for fact finding (hexaf) T Yoneda, J Mitchell, J Welbl, P Stenetorp, S Riedel Proceedings of the First Workshop on Fact Extraction and VERification (FEVER …, 2018 | 121 | 2018 |
| Frequency-guided word substitutions for detecting textual adversarial examples M Mozes, P Stenetorp, B Kleinberg, LD Griffin arXiv preprint arXiv:2004.05887, 2020 | 99 | 2020 |
| An attentive neural architecture for fine-grained entity type classification S Shimaoka, P Stenetorp, K Inui, S Riedel arXiv preprint arXiv:1604.05525, 2016 | 99 | 2016 |
| Learning reasoning strategies in end-to-end differentiable proving P Minervini, S Riedel, P Stenetorp, E Grefenstette, T Rocktäschel International Conference on Machine Learning, 6938-6949, 2020 | 98 | 2020 |
| Improving question answering model robustness with synthetic adversarial data generation M Bartolo, T Thrush, R Jia, S Riedel, P Stenetorp, D Kiela arXiv preprint arXiv:2104.08678, 2021 | 95 | 2021 |
| Assessing the benchmarking capacity of machine reading comprehension datasets S Sugawara, P Stenetorp, K Inui, A Aizawa Proceedings of the AAAI Conference on Artificial Intelligence 34 (05), 8918-8927, 2020 | 81 | 2020 |
| Generating data to mitigate spurious correlations in natural language inference datasets Y Wu, M Gardner, P Stenetorp, P Dasigi arXiv preprint arXiv:2203.12942, 2022 | 75 | 2022 |
| 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 | 75 | 2021 |
| Task-oriented learning of word embeddings for semantic relation classification K Hashimoto, P Stenetorp, M Miwa, Y Tsuruoka arXiv preprint arXiv:1503.00095, 2015 | 70 | 2015 |
| Axcell: Automatic extraction of results from machine learning papers M Kardas, P Czapla, P Stenetorp, S Ruder, S Riedel, R Taylor, R Stojnic arXiv preprint arXiv:2004.14356, 2020 | 69 | 2020 |
