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Question answering on tabular data (a.k.a TableQA), which aims at generating answers to questions grounded on a provided table, has gained significant attention recently.
On the variance of the adaptive learning rate and beyond
Liyuan Liu, Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, and Jiawei Han. 2019 · 1908
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Question answering from frequently asked question files: Experiences with the faq finder system
Robin D Burke, Kristian J Hammond, Vladimir Kulyukin, Steven L Lytinen, Noriko Tomuro, and Scott Schoenberg. 1997 · 1997
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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Hybridqa: A dataset of multi-hop question answering over tabular and textual data
Wenhu Chen, Hanwen Zha, Zhiyu Chen, Wenhan Xiong, Hong Wang, and William Wang. 2020b · 2004
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Meteor: An automatic metric for mt evaluation with improved correlation with human judgments
Satanjeev Banerjee and Alon Lavie. 2005 · 2005
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The probabilistic relevance framework: BM25 and beyond
Stephen Robertson and Hugo Zaragoza. 2009 · 2009
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Open question answering over tables and text
Wenhu Chen, Ming-Wei Chang, Eva Schlinger, William Wang, and William W Cohen. 2020a · 2010
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Information extraction over structured data: Question answering with Freebase
Xuchen Yao and Benjamin Van Durme. 2014 · 2014
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Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel. 2015 · 2015
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Compositional semantic parsing on semi-structured tables
Panupong Pasupat and Percy Liang. 2015 · 2015
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A copy-augmented sequence-to-sequence architecture gives good performance on task-oriented dialogue
Mihail Eric and Christopher Manning. 2017 · 2017
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An end-to-end model for question answering over knowledge base with cross-attention combining global knowledge
Yanchao Hao, Yuanzhe Zhang, Kang Liu, Shizhu He, Zhanyi Liu, Hua Wu, and Jun Zhao. 2017 · 2017
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Generating natural answers by incorporating copying and retrieving mechanisms in sequence-to-sequence learning
Shizhu He, Cao Liu, Kang Liu, and Jun Zhao. 2017 · 2017
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Neural symbolic machines: Learning semantic parsers on Freebase with weak supervision
Chen Liang, Jonathan Berant, Quoc Le, Kenneth D. Forbus, and Ni Lao. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 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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Deep graph convolutional encoders for structured data to text generation
Diego Marcheggiani and Laura Perez-Beltrachini. 2018 · 2018
Cited alongside, same era.
Image transformer
Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Noam Shazeer, Alexander Ku, and Dustin Tran. 2018 · 2018
Cited alongside, same era.
Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-SQL task
Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, Zilin Zhang, and Dragomir Radev. 2018 · 2018
Cited alongside, same era.
Handling divergent reference texts when evaluating table-to-text generation
Bhuwan Dhingra, Manaal Faruqui, Ankur Parikh, Ming-Wei Chang, Dipanjan Das, and William Cohen. 2019 · 2019
Cited alongside, same era.
Unified language model pre-training for natural language understanding and generation
Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019 · 2019
Cited alongside, same era.
Capturing row and column semantics in transformer based question answering over tables
Michael Glass, Mustafa Canim, Alfio Gliozzo, Saneem Chemmengath, Vishwajeet Kumar, Rishav Chakravarti, Avi Sil, Feifei Pan, Samarth Bharadwaj, and Nicolas Rodolfo Fauceglia. 2021 · 2021
Later among the works it cites.
Open domain question answering over tables via dense retrieval
Jonathan Herzig, Thomas Müller, Syrine Krichene, and Julian Eisenschlos. 2021 · 2021
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Leveraging passage retrieval with generative models for open domain question answering
Gautier Izacard and Edouard Grave. 2021 · 2021
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Bertgcn: Transductive text classification by combining gcn and bert
Yuxiao Lin, Yuxian Meng, Xiaofei Sun, Qinghong Han, Kun Kuang, Jiwei Li, and Fei Wu. 2021 · 2021
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Dense hierarchical retrieval for open-domain question answering
Ye Liu, Kazuma Hashimoto, Yingbo Zhou, Semih Yavuz, Caiming Xiong, and Philip Yu. 2021a · 2021
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Cited alongside, same era.
Enhancing AMR-to-text generation with dual graph representations
Leonardo F. R. Ribeiro, Claire Gardent, and Iryna Gurevych. 2019 · 2019
Cited alongside, same era.
TaPas: Weakly supervised table parsing via pre-training
Jonathan Herzig, Pawel Krzysztof Nowak, Thomas Müller, Francesco Piccinno, and Julian Eisenschlos. 2020 · 2020
Cited alongside, same era.
BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
Cited alongside, same era.
Towards faithful neural table-to-text generation with content-matching constraints
Zhenyi Wang, Xiaoyang Wang, Bang An, Dong Yu, and Changyou Chen. 2020 · 2020
Cited alongside, same era.
TaBERT: Pretraining for joint understanding of textual and tabular data
Pengcheng Yin, Graham Neubig, Wen tau Yih, and Sebastian Riedel. 2020 · 2020
Cited alongside, same era.
CLTR: An end-to-end, transformer-based system for cell-level table retrieval and table question answering
Feifei Pan, Mustafa Canim, Michael Glass, Alfio Gliozzo, and Peter Fox. 2021 · 2021
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Multimodalqa: Complex question answering over text, tables and images
Alon Talmor, Ori Yoran, Amnon Catav, Dan Lahav, Yizhong Wang, Akari Asai, Gabriel Ilharco, Hannaneh Hajishirzi, and Jonathan Berant. 2021 · 2021
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Gnn is a counter? revisiting gnn for question answering
Kuan Wang, Yuyu Zhang, Diyi Yang, Le Song, and Tao Qin. 2021 · 2021
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QA-GNN: Reasoning with language models and knowledge graphs for question answering
Michihiro Yasunaga, Hongyu Ren, Antoine Bosselut, Percy Liang, and Jure Leskovec. 2021 · 2021
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Gra{pp}a: Grammar-augmented pre-training for table semantic parsing
Tao Yu, Chien-Sheng Wu, Xi Victoria Lin, bailin wang, Yi Chern Tan, Xinyi Yang, Dragomir Radev, richard socher, and Caiming Xiong. 2021 · 2021
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Attend, memorize and generate: Towards faithful table-to-text generation in few shots
Wenting Zhao, Ye Liu, Yao Wan, and Philip Yu. 2021 · 2021
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TAT-QA: A question answering benchmark on a hybrid of tabular and textual content in finance
Fengbin Zhu, Wenqiang Lei, Youcheng Huang, Chao Wang, Shuo Zhang, Jiancheng Lv, Fuli Feng, and Tat-Seng Chua. 2021 · 2021
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Uni-parser: Unified semantic parser for question answering on knowledge base and database
Ye Liu, Semih Yavuz, Rui Meng, Dragomir Radev, Caiming Xiong, and Yingbo Zhou. 2022b · 2022
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FeTaQA: Free-form Table Question Answering
Linyong Nan, Chiachun Hsieh, Ziming Mao, Xi Victoria Lin, Neha Verma, Rui Zhang, Wojciech Kryściński, Hailey Schoelkopf, Riley Kong, Xiangru Tang, Mutethia Mutuma, Ben Rosand, Isabel Trindade, Renusree Bandaru, Jacob Cunningham, Caiming Xiong, Dragomir Radev, and Dragomir Radev. 2022 · 2022
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End-to-end table question answering via retrieval-augmented generation
Feifei Pan, Mustafa Canim, Michael Glass, Alfio Gliozzo, and James Hendler. 2022 · 2022
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Continuous prompt tuning based textual entailment model for e-commerce entity typing
Yibo Wang, Congying Xia, Guan Wang, and Philip S. Yu. 2022 · 2022
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Large language models are few(1)-shot table reasoners
Wenhu Chen. 2023 · 2023
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