| Dynet: The dynamic neural network toolkit G Neubig, C Dyer, Y Goldberg, A Matthews, W Ammar, A Anastasopoulos, ... arXiv preprint arXiv:1701.03980, 2017 | 273 | 2017 |
| Understanding objects in detail with fine-grained attributes A Vedaldi, S Mahendran, S Tsogkas, S Maji, R Girshick, J Kannala, ... Proceedings of the IEEE conference on computer vision and pattern …, 2014 | 129 | 2014 |
| Understanding learning dynamics of language models with SVCCA N Saphra, A Lopez arXiv preprint arXiv:1811.00225, 2018 | 95 | 2018 |
| Understanding privacy-related questions on stack overflow M Tahaei, K Vaniea, N Saphra Proceedings of the 2020 CHI conference on human factors in computing systems …, 2020 | 91 | 2020 |
| The multiberts: Bert reproductions for robustness analysis T Sellam, S Yadlowsky, J Wei, N Saphra, A D'Amour, T Linzen, J Bastings, ... arXiv preprint arXiv:2106.16163, 2021 | 85 | 2021 |
| An algerian arabic-french code-switched corpus R Cotterell, A Renduchintala, N Saphra, C Callison-Burch Workshop on free/open-source arabic corpora and corpora processing tools …, 2014 | 67 | 2014 |
| State-of-the-art generalisation research in NLP: a taxonomy and review D Hupkes, M Giulianelli, V Dankers, M Artetxe, Y Elazar, T Pimentel, ... arXiv preprint arXiv:2210.03050, 2022 | 56 | 2022 |
| Pareto probing: Trading off accuracy for complexity T Pimentel, N Saphra, A Williams, R Cotterell arXiv preprint arXiv:2010.02180, 2020 | 55 | 2020 |
| Linear connectivity reveals generalization strategies J Juneja, R Bansal, K Cho, J Sedoc, N Saphra arXiv preprint arXiv:2205.12411, 2022 | 45 | 2022 |
| A taxonomy and review of generalization research in NLP D Hupkes, M Giulianelli, V Dankers, M Artetxe, Y Elazar, T Pimentel, ... Nature Machine Intelligence 5 (10), 1161-1174, 2023 | 39 | 2023 |
| A framework for (under) specifying dependency syntax without overloading annotators N Schneider, B O'Connor, N Saphra, D Bamman, M Faruqui, NA Smith, ... arXiv preprint arXiv:1306.2091, 2013 | 31 | 2013 |
| A non-linear structural probe JC White, T Pimentel, N Saphra, R Cotterell arXiv preprint arXiv:2105.10185, 2021 | 23 | 2021 |
| Sudden drops in the loss: Syntax acquisition, phase transitions, and simplicity bias in MLMs A Chen, R Schwartz-Ziv, K Cho, ML Leavitt, N Saphra arXiv preprint arXiv:2309.07311, 2023 | 22 | 2023 |
| LSTMs compose (and learn) bottom-up N Saphra, A Lopez arXiv preprint arXiv:2010.04650, 2020 | 15 | 2020 |
| Benchmarking compositionality with formal languages J Valvoda, N Saphra, J Rawski, A Williams, R Cotterell arXiv preprint arXiv:2208.08195, 2022 | 14 | 2022 |
| First tragedy, then parse: History repeats itself in the new era of large language models N Saphra, E Fleisig, K Cho, A Lopez arXiv preprint arXiv:2311.05020, 2023 | 13 | 2023 |
| Amrica: an amr inspector for cross-language alignments N Saphra, A Lopez Proceedings of the 2015 conference of the north american chapter of the …, 2015 | 13 | 2015 |
| Language models learn POS first N Saphra, A Lopez Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and …, 2018 | 12 | 2018 |
| One venue, two conferences: The separation of Chinese and American citation networks B Zhao, Y Gu, JZ Forde, N Saphra arXiv preprint arXiv:2211.12424, 2022 | 3 | 2022 |
| Training dynamics of neural language models N Saphra The University of Edinburgh, 2021 | 3 | 2021 |
