🔗 Who cites «Compositional Semantic Parsing on Semi-Structured Tables»

404 papers of our library cite it (of the 140,884 whose bibliographies we read) · 38 with a page here, the most cited first · ← the paper

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4. TaPas: Weakly Supervised Table Parsing via Pre-trainingJonathan Herzig, Pawel Krzysztof Nowak, Thomas Müller, et al.2020 · 844 citations
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7. TaBERT: Pretraining for Joint Understanding of Textual and Tabular DataPengcheng Yin, Graham Neubig, Wen-tau Yih, et al.2020 · 690 citations
8. Learning to Compose Neural Networks for Question AnsweringJacob Andreas, Marcus Rohrbach, Trevor Darrell, et al.2016 · 547 citations
9. FinQA: A Dataset of Numerical Reasoning over Financial DataZhiyu Chen, Wenhu Chen, Charese Smiley, et al.2021 · 510 citations
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15. HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual DataWenhu Chen, Hanwen Zha, Zhiyu Chen, et al.2020 · 376 citations
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20. From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal LikelihoodKelvin Guu, Panupong Pasupat, Evan Liu, et al.2017 · 230 citations
21. SParC: Cross-Domain Semantic Parsing in ContextTao Yu, Rui Zhang, Michihiro Yasunaga, et al.2019 · 227 citations
22. Break It Down: A Question Understanding BenchmarkTomer Wolfson, Mor Geva, Ankit Gupta, et al.2020 · 218 citations
23. Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical ReasoningPan Lu, Liang Qiu, Kai-Wei Chang, et al.2022 · 209 citations
24. Logical Natural Language Generation from Open-Domain TablesWenhu Chen, Jianshu Chen, Yu Su, et al.2020 · 209 citations
25. What matters when building vision-language models?Hugo Laurençon, Leo Tronchon, Matthieu Cord, et al.2024 · 195 citations
26. Large Language Models are few(1)-shot Table ReasonersWenhu Chen2023 · 182 citations
27. Evaluating Large Language Models: A Comprehensive SurveyZishan Guo, Renren Jin, Chuang Liu, et al.2023 · 177 citations
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29. Structure-Grounded Pretraining for Text-to-SQLXiang Deng, Ahmed Hassan Awadallah, Christopher Meek, et al.2021 · 166 citations
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37. Synthetic Function Demonstrations Improve Generation in Low-Resource Programming LanguagesNick McKenna, Xinnuo Xu, Jack Williams, et al.2026 · 1 citation
38. A Teacher-Student Approach to Creating Verified Synthetic Clarification and Correction Dialogues for TableQA TasksChristian Poelitz, Nick McKenna2026

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