Order-Planning Neural Text Generation From Structured Data


  • Lei Sha Peking University
  • Lili Mou University of Waterloo
  • Tianyu Liu Peking University
  • Pascal Poupart University of Waterloo
  • Sujian Li Peking University
  • Baobao Chang Peking University
  • Zhifang Sui Peking University


text generation, order planning, neural network


Generating texts from structured data (e.g., a table) is important for various natural language processing tasks such as question answering and dialog systems. In recent studies, researchers use neural language models and encoder-decoder frameworks for table-to-text generation. However, these neural network-based approaches typically do not model the order of content during text generation. When a human writes a summary based on a given table, he or she would probably consider the content order before wording. In this paper, we propose an order-planning text generation model, where order information is explicitly captured by link-based attention. Then a self-adaptive gate combines the link-based attention with traditional content-based attention. We conducted experiments on the WikiBio dataset and achieve higher performance than previous methods in terms of BLEU, ROUGE, and NIST scores; we also performed ablation tests to analyze each component of our model.




How to Cite

Sha, L., Mou, L., Liu, T., Poupart, P., Li, S., Chang, B., & Sui, Z. (2018). Order-Planning Neural Text Generation From Structured Data. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11947