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, March 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: March 03, 2021Submitted by Sabina P
