Order-Planning Neural Text Generation From Structured Data
- Lei Sha
- Lili Mou
- Tianyu Liu
- Pascal Poupart, University of Waterloo
- Sujian Li
- Baobao Chang
- Zhifang Sui
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.
Citation
L. Sha, L. Mou, T. Liu, P. Poupart, S. Li, B. Chang, Z. Sui. "Order-Planning Neural Text Generation From Structured Data". National Conference on Artificial Intelligence (AAAI), pp 5414-5421, February 2018.Keywords: | text generation, order planning, neural network |
Category: | In Conference |
Web Links: | AAAI |
BibTeX
@incollection{Sha+al:AAAI18, author = {Lei Sha and Lili Mou and Tianyu Liu and Pascal Poupart and Sujian Li and Baobao Chang and Zhifang Sui}, title = {Order-Planning Neural Text Generation From Structured Data}, Pages = {5414-5421}, booktitle = {National Conference on Artificial Intelligence (AAAI)}, year = 2018, }Last Updated: February 03, 2021
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