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People primarily consult tables to conduct data analysis or answer specific questions.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 1901
Earlier work this paper cites.
Design of a knowledge-based report generator
Karen Kukich. 1983 · 1983
Earlier work this paper cites.
The interpretation of tables in texts
Matthew F. Hurst. 2000 · 2000
Earlier work this paper cites.
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Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
Automatic evaluation of summaries using n-gram co-occurrence statistics
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Earlier work this paper cites.
METEOR: An automatic metric for MT evaluation with improved correlation with human judgments
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Hoa Trang Dang. 2006 · 2005
Earlier work this paper cites.
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Justus J Randolph. 2005 · 2005
Earlier work this paper cites.
Compositional semantic parsing on semi-structured tables
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Earlier work this paper cites.
Neural text generation from structured data with application to the biography domain
Rémi Lebret, David Grangier, and Michael Auli. 2016 · 2016
Earlier work this paper cites.
Search-based neural structured learning for sequential question answering
Mohit Iyyer, Wen-tau Yih, and Ming-Wei Chang. 2017 · 2017
Earlier work this paper cites.
A call for clarity in reporting BLEU scores
Matt Post. 2018 · 2018
Earlier work this paper cites.
Seq2SQL: Generating structured queries from natural language using reinforcement learning
Victor Zhong, Caiming Xiong, and Richard Socher. 2018 · 2018
Earlier work this paper cites.
Sentence-BERT: Sentence embeddings using Siamese BERT-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
Earlier work this paper cites.
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. 2020c · 2020
Earlier work this paper cites.
TaPas: Weakly supervised table parsing via pre-training
Jonathan Herzig, Pawel Krzysztof Nowak, Thomas Müller, Francesco Piccinno, and Julian Eisenschlos. 2020 · 2020
Earlier work this paper cites.
BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
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Earlier work this paper cites.
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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
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CAiRE-COVID: A question answering and query-focused multi-document summarization system for COVID-19 scholarly information management
Dan Su, Yan Xu, Tiezheng Yu, Farhad Bin Siddique, Elham Barezi, and Pascale Fung. 2020 · 2020
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Coarse-to-fine query focused multi-document summarization
Yumo Xu and Mirella Lapata. 2020 · 2020
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Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020 · 2020
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Unifiedskg: Unifying and multi-tasking structured knowledge grounding with text-to-text language models
Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, and Tao Yu. 2022 · 2022
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Document summarization with latent queries
Yumo Xu and Mirella Lapata. 2022 · 2022
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ReasTAP: Injecting table reasoning skills during pre-training via synthetic reasoning examples
Yilun Zhao, Linyong Nan, Zhenting Qi, Rui Zhang, and Dragomir Radev. 2022b · 2022
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TaCube: Pre-computing data cubes for answering numerical-reasoning questions over tabular data
Fan Zhou, Mengkang Hu, Haoyu Dong, Zhoujun Cheng, Fan Cheng, Shi Han, and Dongmei Zhang. 2022 · 2022
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Critic: Large language models can self-correct with tool-interactive critiquing
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Scigen: a dataset for reasoning-aware text generation from scientific tables
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From tables to knowledge: Recent advances in table understanding
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Towards table-to-text generation with numerical reasoning
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