| Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task T Yu, R Zhang, K Yang, M Yasunaga, D Wang, Z Li, J Ma, I Li, Q Yao, ... EMNLP 2018, 2018 | 862 | 2018 |
| Beyond the imitation game: Quantifying and extrapolating the capabilities of language models A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... Transactions on Machine Learning Research, 2022 | 770 | 2022 |
| Typesql: Knowledge-based type-aware neural text-to-sql generation T Yu, Z Li, Z Zhang, R Zhang, D Radev NAACL 2018, 2018 | 266 | 2018 |
| Unifiedskg: Unifying and multi-tasking structured knowledge grounding with text-to-text language models T Xie, CH Wu, P Shi, R Zhong, T Scholak, M Yasunaga, CS Wu, M Zhong, ... EMNLP 2022, 2022 | 229* | 2022 |
| SyntaxSQLNet: Syntax Tree Networks for Complex and Cross Domain Text-to-SQL Task T Yu, M Yasunaga, K Yang, R Zhang, D Wang, Z Li, D Radev EMNLP 2018, 2018 | 209 | 2018 |
| GraPPa: grammar-augmented pre-training for table semantic parsing T Yu, CS Wu, XV Lin, B Wang, YC Tan, X Yang, D Radev, R Socher, ... ICLR 2021, 2021 | 204* | 2021 |
| QMSum: A new benchmark for query-based multi-domain meeting summarization M Zhong, D Yin, T Yu, A Zaidi, M Mutuma, R Jha, AH Awadallah, ... NAACL 2021, 2021 | 195 | 2021 |
| SParC: cross-domain semantic parsing in context T Yu, R Zhang, M Yasunaga, YC Tan, XV Lin, S Li, H Er, I Li, B Pang, ... ACL 2019, 2019 | 174* | 2019 |
| Dart: Open-domain structured data record to text generation L Nan, D Radev, R Zhang, A Rau, A Sivaprasad, C Hsieh, X Tang, A Vyas, ... NAACL 2021, 2020 | 161* | 2020 |
| Twitter sentiment in New York City parks as measure of well-being RA Plunz, Y Zhou, MIC Vintimilla, K Mckeown, T Yu, L Uguccioni, ... Landscape and urban planning 189, 235-246, 2019 | 155 | 2019 |
| Editing-based SQL query generation for cross-domain context-dependent questions R Zhang, T Yu, HY Er, S Shim, E Xue, XV Lin, T Shi, C Xiong, R Socher, ... EMNLP 2019, 2019 | 150* | 2019 |
| Selective annotation makes language models better few-shot learners H Su, J Kasai, CH Wu, W Shi, T Wang, J Xin, R Zhang, M Ostendorf, ... ICLR 2023, 2023 | 131* | 2023 |
| One embedder, any task: Instruction-finetuned text embeddings H Su, J Kasai, Y Wang, Y Hu, M Ostendorf, W Yih, NA Smith, ... ACL 2023, 2023 | 103 | 2023 |
| Binding language models in symbolic languages Z Cheng, T Xie, P Shi, C Li, R Nadkarni, Y Hu, C Xiong, D Radev, ... ICLR 2023, 2023 | 102 | 2023 |
| Zerogen: Efficient zero-shot learning via dataset generation J Ye, J Gao, Q Li, H Xu, J Feng, Z Wu, T Yu, L Kong EMNLP 2022, 2022 | 94 | 2022 |
| DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation Y Lai, C Li, Y Wang, T Zhang, R Zhong, L Zettlemoyer, SW Yih, D Fried, ... ICML 2023, 2023 | 89 | 2023 |
| In-Context Learning for Few-Shot Dialogue State Tracking Y Hu, CH Lee, T Xie, T Yu, NA Smith, M Ostendorf EMNLP Findings 2022, 2022 | 87 | 2022 |
| Cosql: A conversational text-to-sql challenge towards cross-domain natural language interfaces to databases T Yu, R Zhang, HY Er, S Li, E Xue, B Pang, XV Lin, YC Tan, T Shi, Z Li, ... EMNLP 2019, 2019 | 81 | 2019 |
| Semantic evaluation for text-to-SQL with distilled test suites R Zhong, T Yu, D Klein EMNLP 2020, 2020 | 76 | 2020 |
| Folio: Natural language reasoning with first-order logic S Han, H Schoelkopf, Y Zhao, Z Qi, M Riddell, L Benson, L Sun, E Zubova, ... arXiv preprint arXiv:2209.00840, 2022 | 72* | 2022 |
Dragomir RadevProfessor, Computer Science, Yale UniversityE-mailová adresa ověřena na: yale.edu
