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In open domain table-to-text generation, we notice that the unfaithful generation usually contains hallucinated content which can not be aligned to any input table record.
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Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports
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Collective content selection for concept-to-text generation
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Posterior Control of Blackbox Generation
Li, X. L.; and Rush, A. M. 2020 · 2005
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Large-scale simple question generation by template-based seq2seq learning
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Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
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Table-to-Text: Describing Table Region With Natural Language
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Towards Faithful Neural Table-to-Text Generation with Content-Matching Constraints
Wang, Z.; Wang, X.; An, B.; Yu, D.; and Chen, C. 2020b · 2005
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Aggregation via set partitioning for natural language generation
Barzilay, R.; and Lapata, M. 2006 · 2006
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Automatic generation of weather forecast texts using comprehensive probabilistic generation-space models
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Liang, P.; Jordan, M. I.; and Klein, D. 2009 · 2009
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Generative alignment and semantic parsing for learning from ambiguous supervision
Kim, J.; and Mooney, R. J. 2010 · 2010
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Learning to generate move-by-move commentary for chess games from large-scale social forum data
Jhamtani, H.; Gangal, V.; Hovy, E.; Neubig, G.; and Berg-Kirkpatrick, T. 2018 · 2018
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Learning to generate wikipedia summaries for underserved languages from wikidata
Kaffee, L.-A.; Elsahar, H.; Vougiouklis, P.; Gravier, C.; Laforest, F.; Hare, J.; and Simperl, E. 2018 · 2018
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Scalable Micro-planned Generation of Discourse from Structured Data
Laha, A.; Jain, P.; Mishra, A.; and Sankaranarayanan, K. 2018 · 2018
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Table-to-Text Generation by Structure-Aware Seq2seq Learning
Liu, T.; Wang, K.; Sha, L.; Chang, B.; and Sui, Z. 2018 · 2018
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Towards controllable story generation
Peng, N.; Ghazvininejad, M.; May, J.; and Knight, K. 2018 · 2018
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Bootstrapping generators from noisy data
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Data-to-Text Generation with Content Selection and Planning
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Order-Planning Neural Text Generation From Structured Data
Sha, L.; Mou, L.; Liu, T.; Poupart, P.; Li, S.; Chang, B.; and Sui, Z. 2018 · 2018
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Wang, Q.; Pan, X.; Huang, L.; Zhang, B.; Jiang, Z.; Ji, H.; and Knight, K. 2018 · 2018
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Learning neural templates for text generation
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Ranking generated summaries by correctness: An interesting but challenging application for natural language inference
Falke, T.; Ribeiro, L. F.; Utama, P. A.; Dagan, I.; and Gurevych, I. 2019 · 2019
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Assessing the factual accuracy of generated text
Goodrich, B.; Rao, V.; Liu, P. J.; and Saleh, M. 2019 · 2019
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Evaluating the state-of-the-art of end-to-end natural language generation: The E2E NLG Challenge
Dušek, O.; Novikova, J.; and Rieser, V. 2020 · 2020
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Key Fact as Pivot: A Two-Stage Model for Low Resource Table-to-Text Generation
Ma, S.; Yang, P.; Liu, T.; Li, P.; Zhou, J.; and Sun, X. 2019 · 2057
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