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Controlled table-to-text generation seeks to generate natural language descriptions for highlighted subparts of a table.
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Sam Wiseman, Stuart M Shieber, and Alexander M Rush. 2017 · 2017
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A mixed hierarchical attention based encoder-decoder approach for standard table summarization
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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 · 2018
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Generating descriptions from structured data using a bifocal attention mechanism and gated orthogonalization
Preksha Nema, Shreyas Shetty, Parag Jain, Anirban Laha, Karthik Sankaranarayanan, and Mitesh M Khapra. 2018 · 2018
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Laura Perez-Beltrachini and Mirella Lapata. 2018 · 2018
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Tabfact: A large-scale dataset for table-based fact verification
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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
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Bleurt: Learning robust metrics for text generation
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Transformers: State-of-the-art natural language processing
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On the generalization effects of linear transformations in data augmentation
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Pytorch: An imperative style, high-performance deep learning library
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Tapas: Weakly supervised table parsing via pre-training
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Rotation equivariant graph convolutional network for spherical image classification
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Plan-then-generate: Controlled data-to-text generation via planning
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Table-based fact verification with salience-aware learning
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Hitab: A hierarchical table dataset for question answering and natural language generation
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Key fact as pivot: A two-stage model for low resource table-to-text generation
Shuming Ma, Pengcheng Yang, Tianyu Liu, Peng Li, Jie Zhou, and Xu Sun. 2019 · 2057
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