| Localizing syntactic predictions using recurrent neural network grammars JR Brennan, C Dyer, A Kuncoro, JT Hale Neuropsychologia 146, 107479, 2020 | 611 | 2020 |
| Dynet: The dynamic neural network toolkit G Neubig, C Dyer, Y Goldberg, A Matthews, W Ammar, A Anastasopoulos, ... arXiv preprint, 2017 | 416* | 2017 |
| Scaling language models: Methods, analysis & insights from training gopher JW Rae, S Borgeaud, T Cai, K Millican, J Hoffmann, F Song, J Aslanides, ... arXiv preprint arXiv:2112.11446, 2021 | 257 | 2021 |
| What do recurrent neural network grammars learn about syntax? A Kuncoro, M Ballesteros, L Kong, C Dyer, G Neubig, NA Smith Proceedings of EACL 2017 1, 1249-1258, 2017 | 139 | 2017 |
| LSTMs can learn syntax-sensitive dependencies well, but modeling structure makes them better A Kuncoro, C Dyer, J Hale, D Yogatama, S Clark, P Blunsom Proceedings of the 56th Annual Meeting of the Association for Computational …, 2018 | 134 | 2018 |
| Unsupervised recurrent neural network grammars Y Kim, AM Rush, L Yu, A Kuncoro, C Dyer, G Melis arXiv preprint arXiv:1904.03746, 2019 | 119 | 2019 |
| Finding syntax in human encephalography with beam search J Hale, C Dyer, A Kuncoro, JR Brennan arXiv preprint arXiv:1806.04127, 2018 | 110 | 2018 |
| Distilling an ensemble of greedy dependency parsers into one MST parser A Kuncoro, M Ballesteros, L Kong, C Dyer, NA Smith Proceedings of EMNLP, 1744-1753, 2016 | 79 | 2016 |
| Memory architectures in recurrent neural network language models D Yogatama, Y Miao, G Melis, W Ling, A Kuncoro, C Dyer, P Blunsom International Conference on Learning Representations, 2018 | 58 | 2018 |
| Mind the gap: Assessing temporal generalization in neural language models A Lazaridou, A Kuncoro, E Gribovskaya, D Agrawal, A Liska, T Terzi, ... Advances in Neural Information Processing Systems 34, 29348-29363, 2021 | 52* | 2021 |
| Syntactic structure distillation pretraining for bidirectional encoders A Kuncoro, L Kong, D Fried, D Yogatama, L Rimell, C Dyer, P Blunsom Transactions of the Association for Computational Linguistics 8, 776-794, 2020 | 42* | 2020 |
| Cyprien de Masson d’Autume JW Rae, S Borgeaud, T Cai, K Millican, J Hoffmann, F Song, J Aslanides, ... | 35 | 2021 |
| Scalable syntax-aware language models using knowledge distillation A Kuncoro, C Dyer, L Rimell, S Clark, P Blunsom arXiv preprint arXiv:1906.06438, 2019 | 32 | 2019 |
| Indonlg: Benchmark and resources for evaluating indonesian natural language generation S Cahyawijaya, GI Winata, B Wilie, K Vincentio, X Li, A Kuncoro, S Ruder, ... arXiv preprint arXiv:2104.08200, 2021 | 27 | 2021 |
| The perils of natural behaviour tests for unnatural models: the case of number agreement A Kuncoro, C Dyer, J Hale, P Blunsom Poster presented at Learning Language in Humans and in Machines, Paris, Fr …, 2018 | 8 | 2018 |
| Text genre and training data size in human-like parsing J Hale, A Kuncoro, K Hall, C Dyer, J Brennan Proceedings of the 2019 Conference on Empirical Methods in Natural Language …, 2019 | 5 | 2019 |
| Transformer Grammars: Augmenting Transformer language models with syntactic inductive biases at scale L Sartran, S Barrett, A Kuncoro, M Stanojević, P Blunsom, C Dyer Transactions of the Association for Computational Linguistics 10, 1423-1439, 2022 | 2 | 2022 |
| Do Language Models Learn Commonsense Knowledge? XL Li, A Kuncoro, CM d'Autume, P Blunsom, A Nematzadeh arXiv preprint arXiv:2111.00607, 2021 | 2 | 2021 |
| Dependency parsing with lstms: An empirical evaluation A Kuncoro, Y Sawai, K Duh, Y Matsumoto arXiv preprint arXiv:1604.06529, 2016 | 2 | 2016 |
| A Systematic Investigation of Commonsense Knowledge in Large Language Models XL Li, A Kuncoro, J Hoffmann, C de Masson d’Autume, P Blunsom, ... Proceedings of the 2022 Conference on Empirical Methods in Natural Language …, 2022 | 1 | 2022 |
Chris DyerDeepMind, Carnegie MellonVerifierad e-postadress på google.com
