As the development of the encoder-decoder architecture, researchers are able
to study the text generation tasks with broader types of data. Among them,
KB-to-text aims at converting a set of knowledge triples into human readable
sentences. In the original setting, the task assumes that the input triples and
the text are exactly aligned in the perspective of the embodied
knowledge/information. In this paper, we extend this setting and explore how to
facilitate the trained model to generate more informative text, namely,
containing more information about the triple entities but not conveyed by the
input triples. To solve this problem, we propose a novel memory augmented
generator that employs a memory network to memorize the useful knowledge
learned during the training and utilizes such information together with the
input triples to generate text in the operational or testing phase. We derive a
dataset from WebNLG for our new setting and conduct extensive experiments to
investigate the effectiveness of our model as well as uncover the intrinsic
characteristics of the setting.

本研究提出使用记忆网络作为增强模型来生成 KB-to-text 中的人类可读句子，实现更多信息的传递。通过从 WebNLG 中获取数据集，并对模型进行多次的评测和实验，证明了本文所提出的解决方案的有效性以及内在特性的探究。