| Natural questions: a benchmark for question answering research T Kwiatkowski, J Palomaki, O Redfield, M Collins, A Parikh, C Alberti, ... | 1589 | 2019 |
| Matching the blanks: Distributional similarity for relation learning LB Soares, N FitzGerald, J Ling, T Kwiatkowski arXiv preprint arXiv:1906.03158, 2019 | 709 | 2019 |
| BoolQ: Exploring the surprising difficulty of natural yes/no questions C Clark, K Lee, MW Chang, T Kwiatkowski, M Collins, K Toutanova arXiv preprint arXiv:1905.10044, 2019 | 499 | 2019 |
| Scaling Semantic Parsers with On-the-fly Ontology Matching T Kwiatkowski, E Choi, Y Artzi, L Zettlemoyer | 388 | 2013 |
| Inducing probabilistic CCG grammars from logical form with higher-order unification T Kwiatkowksi, L Zettlemoyer, S Goldwater, M Steedman Proceedings of the 2010 conference on empirical methods in natural language …, 2010 | 384 | 2010 |
| TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages JH Clark, E Choi, M Collins, D Garrette, T Kwiatkowski, V Nikolaev, ... Transactions of the Association for Computational Linguistics 8, 454-470, 2020 | 354 | 2020 |
| Lexical generalization in CCG grammar induction for semantic parsing T Kwiatkowski, L Zettlemoyer, S Goldwater, M Steedman Proceedings of the 2011 Conference on Empirical Methods in Natural Language …, 2011 | 282 | 2011 |
| Transforming dependency structures to logical forms for semantic parsing S Reddy, O Täckström, M Collins, T Kwiatkowski, D Das, M Steedman, ... Transactions of the Association for Computational Linguistics 4, 127-140, 2016 | 199 | 2016 |
| Inherent disagreements in human textual inferences E Pavlick, T Kwiatkowski Transactions of the Association for Computational Linguistics 7, 677-694, 2019 | 178 | 2019 |
| Real-time open-domain question answering with dense-sparse phrase index M Seo, J Lee, T Kwiatkowski, AP Parikh, A Farhadi, H Hajishirzi arXiv preprint arXiv:1906.05807, 2019 | 153 | 2019 |
| Learning recurrent span representations for extractive question answering K Lee, S Salant, T Kwiatkowski, A Parikh, D Das, J Berant arXiv preprint arXiv:1611.01436, 2016 | 153 | 2016 |
| Entities as experts: Sparse memory access with entity supervision T Févry, LB Soares, N FitzGerald, E Choi, T Kwiatkowski arXiv preprint arXiv:2004.07202, 2020 | 123 | 2020 |
| Bootstrapping language acquisition O Abend, T Kwiatkowski, NJ Smith, S Goldwater, M Steedman Cognition 164, 116-143, 2017 | 116 | 2017 |
| A probabilistic model of syntactic and semantic acquisition from child-directed utterances and their meanings T Kwiatkowski, M Steedman, L Zettlemoyer, S Goldwater Proceedings of the 13th Conference of the European Chapter of the ACL (EACL …, 2012 | 97 | 2012 |
| 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 | 57 | 2021 |
| Phrase-indexed question answering: A new challenge for scalable document comprehension M Seo, T Kwiatkowski, AP Parikh, A Farhadi, H Hajishirzi arXiv preprint arXiv:1804.07726, 2018 | 57 | 2018 |
| Decontextualization: Making sentences stand-alone E Choi, J Palomaki, M Lamm, T Kwiatkowski, D Das, M Collins Transactions of the Association for Computational Linguistics 9, 447-461, 2021 | 54 | 2021 |
| Morpho-syntactic lexical generalization for CCG semantic parsing A Wang, T Kwiatkowski, L Zettlemoyer Proceedings of the 2014 Conference on Empirical Methods in Natural Language …, 2014 | 46 | 2014 |
| Scalable semantic parsing with partial ontologies E Choi, T Kwiatkowski, L Zettlemoyer Proceedings of the 53rd Annual Meeting of the Association for Computational …, 2015 | 45 | 2015 |
| Multi-mention learning for reading comprehension with neural cascades S Swayamdipta, AP Parikh, T Kwiatkowski arXiv preprint arXiv:1711.00894, 2017 | 35 | 2017 |
Luke ZettlemoyerUniversity of Washington; Meta AIVerified email at cs.washington.edu
