Commonsense generation aims at generating plausible everyday scenario description based on a set of provided concepts. Digging the relationship of concepts from scratch is non-trivial, therefore, we retrieve prototypes from external knowledge to assist the understanding of the scenario for better description generation. We integrate two additional modules, namely position indicator and scaling module, into the pretrained encoder-decoder model for prototype modeling to enhance the knowledge injection procedure. We conduct experiment on CommonGen benchmark, and experimental results show that our method significantly improves the performance on all the metrics.
翻译:常识生成旨在根据一套提供的概念产生可信的日常情景描述。从零开始挖掘概念的关系并非三重概念,因此,我们从外部知识中提取原型,以帮助了解情景,更好地生成描述。我们把另外两个模块,即位置指标和缩放模块,纳入预先培训的编码器-编码器模型模型模型中,以强化知识注入程序。我们进行了关于共同地球基准的实验,实验结果显示,我们的方法大大改善了所有指标的性能。