| Complex embeddings for simple link prediction T Trouillon, J Welbl, S Riedel, É Gaussier, G Bouchard International conference on machine learning, 2071-2080, 2016 | 3146 | 2016 |
| Convolutional 2d knowledge graph embeddings T Dettmers, P Minervini, P Stenetorp, S Riedel Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 2609 | 2018 |
| Language models as knowledge bases? F Petroni, T Rocktäschel, P Lewis, A Bakhtin, Y Wu, AH Miller, S Riedel arXiv preprint arXiv:1909.01066, 2019 | 2223 | 2019 |
| Retrieval-augmented generation for knowledge-intensive nlp tasks P Lewis, E Perez, A Piktus, F Petroni, V Karpukhin, N Goyal, H Küttler, ... Advances in Neural Information Processing Systems 33, 9459-9474, 2020 | 1881 | 2020 |
| Modeling relations and their mentions without labeled text S Riedel, L Yao, A McCallum Machine Learning and Knowledge Discovery in Databases: European Conference …, 2010 | 1508 | 2010 |
| The CoNLL 2007 shared task on dependency parsing J Nivre, J Hall, S Kübler, R McDonald, J Nilsson, S Riedel, D Yuret Proceedings of the 2007 Joint Conference on Empirical Methods in Natural …, 2007 | 874 | 2007 |
| Relation extraction with matrix factorization and universal schemas S Riedel, L Yao, A McCallum, BM Marlin Proceedings of the 2013 conference of the North American chapter of the …, 2013 | 772 | 2013 |
| 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 | 637 | 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 | 524 | 2018 |
| Fact checking: Task definition and dataset construction A Vlachos, S Riedel Proceedings of the ACL 2014 workshop on language technologies and …, 2014 | 487 | 2014 |
| End-to-end differentiable proving T Rocktäschel, S Riedel Advances in neural information processing systems 30, 2017 | 458 | 2017 |
| TaBERT: Pretraining for joint understanding of textual and tabular data P Yin, G Neubig, W Yih, S Riedel arXiv preprint arXiv:2005.08314, 2020 | 456 | 2020 |
| Scalable zero-shot entity linking with dense entity retrieval L Wu, F Petroni, M Josifoski, S Riedel, L Zettlemoyer arXiv preprint arXiv:1911.03814, 2019 | 445 | 2019 |
| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 407 | 2023 |
| MLQA: Evaluating cross-lingual extractive question answering P Lewis, B Oğuz, R Rinott, S Riedel, H Schwenk arXiv preprint arXiv:1910.07475, 2019 | 401 | 2019 |
| Semeval 2017 task 10: Scienceie-extracting keyphrases and relations from scientific publications I Augenstein, M Das, S Riedel, L Vikraman, A McCallum arXiv preprint arXiv:1704.02853, 2017 | 396 | 2017 |
| Autoregressive entity retrieval N De Cao, G Izacard, S Riedel, F Petroni arXiv preprint arXiv:2010.00904, 2020 | 391 | 2020 |
| KILT: a benchmark for knowledge intensive language tasks F Petroni, A Piktus, A Fan, P Lewis, M Yazdani, N De Cao, J Thorne, ... arXiv preprint arXiv:2009.02252, 2020 | 389 | 2020 |
| emoji2vec: Learning emoji representations from their description B Eisner, T Rocktäschel, I Augenstein, M Bošnjak, S Riedel arXiv preprint arXiv:1609.08359, 2016 | 350 | 2016 |
| Atlas: Few-shot learning with retrieval augmented language models G Izacard, P Lewis, M Lomeli, L Hosseini, F Petroni, T Schick, ... arXiv preprint arXiv:2208.03299, 2022 | 336 | 2022 |
Tim RocktäschelProfessor of Artificial Intelligence at UCL, Open-Endedness Team Lead at Google DeepMind在 cs.ucl.ac.uk 的電子郵件地址已通過驗證
