TY - JOUR AU - Sha, Lei AU - Mou, Lili AU - Liu, Tianyu AU - Poupart, Pascal AU - Li, Sujian AU - Chang, Baobao AU - Sui, Zhifang PY - 2018/04/27 Y2 - 2024/03/29 TI - Order-Planning Neural Text Generation From Structured Data JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 32 IS - 1 SE - Main Track: NLP and Machine Learning DO - 10.1609/aaai.v32i1.11947 UR - https://ojs.aaai.org/index.php/AAAI/article/view/11947 SP - AB - <p> 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. </p> ER -