| Retrieval augmented language model pre-training K Guu, K Lee, Z Tung, P Pasupat, M Chang International conference on machine learning, 3929-3938, 2020 | 2087 | 2020 |
| Compositional semantic parsing on semi-structured tables P Pasupat, P Liang arXiv preprint arXiv:1508.00305, 2015 | 795 | 2015 |
| Rarr: Researching and revising what language models say, using language models L Gao, Z Dai, P Pasupat, A Chen, AT Chaganty, Y Fan, VY Zhao, N Lao, ... arXiv preprint arXiv:2210.08726, 2022 | 261 | 2022 |
| Spoc: Search-based pseudocode to code S Kulal, P Pasupat, K Chandra, M Lee, O Padon, A Aiken, PS Liang Advances in Neural Information Processing Systems 32, 2019 | 229 | 2019 |
| From language to programs: Bridging reinforcement learning and maximum marginal likelihood K Guu, P Pasupat, EZ Liu, P Liang arXiv preprint arXiv:1704.07926, 2017 | 213 | 2017 |
| Reinforcement learning on web interfaces using workflow-guided exploration EZ Liu, K Guu, P Pasupat, T Shi, P Liang arXiv preprint arXiv:1802.08802, 2018 | 204 | 2018 |
| Compositional generalization and natural language variation: Can a semantic parsing approach handle both? P Shaw, MW Chang, P Pasupat, K Toutanova arXiv preprint arXiv:2010.12725, 2020 | 197 | 2020 |
| Asgard: A portable architecture for multilingual dialogue systems J Liu, P Pasupat, S Cyphers, J Glass 2013 IEEE International Conference on Acoustics, Speech and Signal …, 2013 | 135 | 2013 |
| Simpler context-dependent logical forms via model projections R Long, P Pasupat, P Liang arXiv preprint arXiv:1606.05378, 2016 | 104 | 2016 |
| Query understanding enhanced by hierarchical parsing structures J Liu, P Pasupat, Y Wang, S Cyphers, J Glass 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, 72-77, 2013 | 91 | 2013 |
| Improving compositional generalization with latent structure and data augmentation L Qiu, P Shaw, P Pasupat, PK Nowak, T Linzen, F Sha, K Toutanova arXiv preprint arXiv:2112.07610, 2021 | 77 | 2021 |
| Unlocking compositional generalization in pre-trained models using intermediate representations J Herzig, P Shaw, MW Chang, K Guu, P Pasupat, Y Zhang arXiv preprint arXiv:2104.07478, 2021 | 73 | 2021 |
| Inferring logical forms from denotations P Pasupat, P Liang arXiv preprint arXiv:1606.06900, 2016 | 68 | 2016 |
| From pixels to ui actions: Learning to follow instructions via graphical user interfaces P Shaw, M Joshi, J Cohan, J Berant, P Pasupat, H Hu, U Khandelwal, ... Advances in Neural Information Processing Systems 36, 34354-34370, 2023 | 66 | 2023 |
| Macro grammars and holistic triggering for efficient semantic parsing Y Zhang, P Pasupat, P Liang arXiv preprint arXiv:1707.07806, 2017 | 60 | 2017 |
| A Conversational Movie Search System Based on Conditional Random Fields. J Liu, S Cyphers, P Pasupat, I McGraw, JR Glass Interspeech, 2454-2457, 2012 | 59 | 2012 |
| Few-shot intent classification and slot filling with retrieved examples D Yu, L He, Y Zhang, X Du, P Pasupat, Q Li arXiv preprint arXiv:2104.05763, 2021 | 56 | 2021 |
| Zero-shot entity extraction from web pages P Pasupat, P Liang Proceedings of the 52nd Annual Meeting of the Association for Computational …, 2014 | 55 | 2014 |
| Evaluating the impact of model scale for compositional generalization in semantic parsing L Qiu, P Shaw, P Pasupat, T Shi, J Herzig, E Pitler, F Sha, K Toutanova arXiv preprint arXiv:2205.12253, 2022 | 54 | 2022 |
| Mapping natural language commands to web elements P Pasupat, TS Jiang, EZ Liu, K Guu, P Liang arXiv preprint arXiv:1808.09132, 2018 | 52 | 2018 |
Kelvin GuuSenior Staff Research Scientist, GoogleE-mailová adresa ověřena na: google.com