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1
Plan-then-Generate: Controlled Data-to-Text Generation via Planning ...
Abstract: Recent developments in neural networks have led to the advance in data-to-text generation. However, the lack of ability of neural models to control the structure of generated output can be limiting in certain real-world applications. In this study, we propose a novel Plan-then-Generate (PlanGen) framework to improve the controllability of neural data-to-text models. Extensive experiments and analyses are conducted on two benchmark datasets, ToTTo and WebNLG. The results show that our model is able to control both the intra-sentence and inter-sentence structure of the generated output. Furthermore, empirical comparisons against previous state-of-the-art methods show that our model improves the generation quality as well as the output diversity as judged by human and automatic evaluations. ... : Accepted to Findings of EMNLP 2021 ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.2108.13740
https://arxiv.org/abs/2108.13740
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Plan-then-Generate: Controlled Data-to-Text Generation via Planning ...
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3
A Generative Model for Joint Natural Language Understanding and Generation ...
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4
Proposition-based summarization with a coherence-driven incremental model ...
Fang, Yimai. - : Apollo - University of Cambridge Repository, 2019
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Proposition-based summarization with a coherence-driven incremental model
Fang, Yimai. - : University of Cambridge, 2018. : Computer Science and Technology, 2018. : Hughes Hall, 2018
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