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The sheer volume of scientific experimental results and complex technical statements, often presented in tabular formats, presents a formidable barrier to individuals acquiring preferred information.
“Building applied natural language generation systems,”
Ehud Reiter and Robert Dale, · 1997
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“The generality/specificity of expertise in scientific reasoning,”
Christian D Schunn and John R Anderson, · 1999
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“The development of scientific reasoning skills,”
Corinne Zimmerman, · 2000
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“An information-theoretic perspective of tf–idf measures q,”
Akiko Aizawa, · 2003
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“The wikipedia xml corpus,”
Ludovic Denoyer and Patrick Gallinari, · 2006
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“Learning and scientific reasoning,”
Lei Bao, Tianfan Cai, Kathy Koenig, Kai Fang, Jing Han, Jing Wang, Qing Liu, Lin Ding, Lili Cui, Ying Luo, et al., · 2009
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“Challenges in data-to-document generation,”
Sam Wiseman, Stuart M Shieber, and Alexander M Rush, · 2017
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“Attention is all you need,”
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin, · 2017
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“Table-to-text generation by structure-aware seq2seq learning,”
Tianyu Liu, Kexiang Wang, Lei Sha, Baobao Chang, and Zhifang Sui, · 2018
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“Logical natural language generation from open-domain tables,”
Wenhu Chen, Jianshu Chen, Yu Su, Zhiyu Chen, and William Yang Wang, · 2020
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Ankur P Parikh, Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui, Bhuwan Dhingra, Diyi Yang, and Dipanjan Das, · 2020
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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, Peter J Liu, et al., · 2020
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“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, et al., · 2020
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“Scigen: a dataset for reasoning-aware text generation from scientific tables,”
Nafise Sadat Moosavi, Andreas Rücklé, Dan Roth, and Iryna Gurevych, · 2021
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“Towards table-to-text generation with numerical reasoning,”
Lya Hulliyyatus Suadaa, Hidetaka Kamigaito, Kotaro Funakoshi, Manabu Okumura, and Hiroya Takamura, · 2021
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“Tsdae: Using transformer-based sequential denoising auto-encoderfor unsupervised sentence embedding learning,”
Kexin Wang, Nils Reimers, and Iryna Gurevych, · 2021
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“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
Cited alongside, same era.
“Chain-of-thought prompting elicits reasoning in large language models,”
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al., · 2022
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