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Table Question Answering (TQA) aims at composing an answer to a question based on tabular data.
Measuring nominal scale agreement among many raters
Joseph L. Fleiss. 1971 · 1971
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An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers
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Compositional semantic parsing on semi-structured tables
Panupong Pasupat and Percy Liang. 2015 · 2015
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Search-based neural structured learning for sequential question answering
Mohit Iyyer, Wen tau Yih, and Ming-Wei Chang. 2017 · 2017
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Seq2sql: Generating structured queries from natural language using reinforcement learning
Victor Zhong, Caiming Xiong, and Richard Socher. 2017 · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Tapas: Weakly supervised table parsing via pre-training
Jonathan Herzig, Pawel Krzysztof Nowak, Thomas Müller, Francesco Piccinno, and Julian Martin Eisenschlos. 2020 · 2020
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Tablex: A benchmark dataset for structure and content information extraction from scientific tables
Harsh Desai, Pratik Kayal, and Mayank Kumar Singh. 2021 · 2021
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Capturing row and column semantics in transformer based question answering over tables
Michael R. Glass, Mustafa Canim, A. Gliozzo, Saneem A. Chemmengath, Rishav Chakravarti, Avirup Sil, Feifei Pan, Samarth Bharadwaj, and Nicolas Rodolfo Fauceglia. 2021 · 2021
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Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021 · 2021
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Tapex: Table pre-training via learning a neural sql executor
Qian Liu, Bei Chen, Jiaqi Guo, Zeqi Lin, and Jian-Guang Lou. 2021 · 2021
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Tat-qa: A question answering benchmark on a hybrid of tabular and textual content in finance
Tableformer: Robust transformer modeling for table-text encoding
Jingfeng Yang, Aditya Gupta, Shyam Upadhyay, Luheng He, Rahul Goel, and Shachi Paul. 2022 · 2022
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Mubashara Akhtar, Abhilash Shankarampeta, Vivek Gupta, Arpit Patil, Oana Cocarascu, and Elena Simperl. 2023 · 2023
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S3hqa: A three-stage approach for multi-hop text-table hybrid question answering
Fangyu Lei, Xiang Lorraine Li, Yifan Wei, Shizhu He, Yiming Huang, Jun Zhao, and Kang Liu. 2023 · 2023
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An inner table retriever for robust table question answering
Weizhe Lin, Rexhina Blloshmi, Bill Byrne, Adrià de Gispert, and Gonzalo Iglesias. 2023 · 2023
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Lever: Learning to verify language-to-code generation with execution
Ansong Ni, Srini Iyer, Dragomir R. Radev, Ves Stoyanov, Wen tau Yih, Sida I. Wang, and Xi Victoria Lin. 2023 · 2023
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Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng, and Tat seng Chua. 2021 · 2021
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Binding language models in symbolic languages
Zhoujun Cheng, Tianbao Xie, Peng Shi, Chengzu Li, R.K. Nadkarni, Yushi Hu, Caiming Xiong, Dragomir R. Radev, Marilyn Ostendorf, Luke Zettlemoyer, Noah A. Smith, and Tao Yu. 2022 · 2022
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OmniTab: Pretraining with natural and synthetic data for few-shot table-based question answering
Zhengbao Jiang, Yi Mao, Pengcheng He, Graham Neubig, and Weizhu Chen. 2022a · 2022
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AssistSR: Task-oriented video segment retrieval for personal AI assistant
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A causal framework to quantify the robustness of mathematical reasoning with language models
Alessandro Stolfo, Zhijing Jin, Kumar Shridhar, Bernhard Scholkopf, and Mrinmaya Sachan. 2022 · 2022
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Is my model using the right evidence? systematic probes for examining evidence-based tabular reasoning
Vivek Gupta, Riyaz A. Bhat, Atreya Ghosal, Manish Shrivastava, Maneesh Singh, and Vivek Srikumar. 2022a
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Right for the right reason: Evidence extraction for trustworthy tabular reasoning
Vivek Gupta, Shuo Zhang, Alakananda Vempala, Yujie He, Temma Choji, and Vivek Srikumar. 2022b
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Omnitab: Pretraining with natural and synthetic data for few-shot table-based question answering
Zhengbao Jiang, Yi Mao, Pengcheng He, Graham Neubig, and Weizhu Chen. 2022b
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Large language models are versatile decomposers: Decomposing evidence and questions for table-based reasoning
Yunhu Ye, Binyuan Hui, Min Yang, Binhua Li, Fei Huang, and Yongbin Li. 2023 · 2023
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Reactable: Enhancing react for table question answering
Yunjia Zhang, Jordan Henkel, Avrilia Floratou, Joyce Cahoon, Shaleen Deep, and Jignesh M. Patel. 2023 · 2023
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Robut: A systematic study of table qa robustness against human-annotated adversarial perturbations
Yilun Zhao, Chen Zhao, Linyong Nan, Zhenting Qi, Wenlin Zhang, Xiangru Tang, Boyu Mi, and Dragomir R. Radev. 2023 · 2023
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