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Logical Table-to-Text (LT2T) generation is tasked with generating logically faithful sentences from tables.
Measuring nominal scale agreement among many raters
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De-confounded variational encoder-decoder for logical table-to-text generation
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PLOG: Table-to-logic pretraining for logical table-to-text generation
Ao Liu, Haoyu Dong, Naoaki Okazaki, Shi Han, and Dongmei Zhang. 2022a · 2022
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HybriDialogue: An information-seeking dialogue dataset grounded on tabular and textual data
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Diversity enhanced table-to-text generation via type control
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Scigen: a dataset for reasoning-aware text generation from scientific tables
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Logical natural language generation from open-domain tables
Wenhu Chen, Jianshu Chen, Yu Su, Zhiyu Chen, and William Yang Wang. 2020a
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Tabfact: A large-scale dataset for table-based fact verification
Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou, and William Yang Wang. 2020b
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Logic2text: High-fidelity natural language generation from logical forms
Zhiyu Chen, Wenhu Chen, Hanwen Zha, Xiyou Zhou, Yunkai Zhang, Sairam Sundaresan, and William Yang Wang. 2020d
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TAPEX: Table pre-training via learning a neural SQL executor
Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, and Jian-Guang Lou. 2022b
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Yotam Perlitz, Liat Ein-Dot, Dafna Sheinwald, Noam Slonim, and Michal Shmueli-Scheuer. 2022 · 2022
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ReasTAP: Injecting table reasoning skills during pre-training via synthetic reasoning examples
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Data-to-text generation with entity modeling
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