Plan-then-Generate: Controlled Data-to-Text Generation via Planning
By Yixuan Su, David Vandyke, Sihui Wang, Yimai Fang, Nigel Collier. 2021
95 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Information Systems, Natural Language Processing
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Citations in the corpus
- Survey of Hallucination in Natural Language Generation (2022, methodology): “Similarly, Plan-then-Generate [146] consists of a content planner and a sequence generator and proposes a structure-aware reinforcement learning (RL) objective that encourages the generated output to adhere to the given content plan.”
- Control Prefixes for Parameter-Efficient Text Generation (2021, background): “Su et al. (2021) were able control both the intra-text sentence and inter-sentence structure of generated output.”
- A Survey of Controllable Text Generation Using Transformer-based Pre-trained Language Models (2022, background): “For instance, the lexical-level overlap between the reference text containing the control elements and the generated text can be used to reflect how well the control conditions are met in the task of table/graph to text [102, 111, 112, 128, 131, 165] and story generation [33, 40].”
- Language Models Can See: Plugging Visual Controls in Text Generation (2022, background): “Such advancements have led to significant progresses in a wide range of natural language understanding (NLU) tasks [44, 87, 12, 38] and text generation tasks tasks [60, 39, 61, 71, 76, 75, 92, 74, 77].”