KGR4: Retrieval, Retrospect, Refine and Rethink for Commonsense Generation

Generative commonsense reasoning requires machines to generate sentences describing an everyday scenario given several concepts, which has attracted much attention recently. However, existing models cannot perform as well as humans, since sentences they produce are often implausible and grammatically incorrect. In this paper, inspired by the process of humans creating sentences, we propose a novel Knowledgeenhanced Commonsense Generation framework, termed KGR4, consisting of four stages: Retrieval, Retrospect, Refine, Rethink. Under this framework, we first perform retrieval to search for relevant sentences from external corpus as the prototypes. Then, we train the generator that either edits or copies these prototypes to generate candidate sentences, of which potential errors will be fixed by an autoencoderbased refiner. Finally, we select the output sentence from candidate sentences produced by generators with different hyper-parameters. Experimental results and in-depth analysis on the CommonGen benchmark strongly demonstrate the effectiveness of our framework. Particularly, KGR4 obtains 33. 56 SPICE points in the official leaderboard, outperforming the previously-reported best result by 2. 49 SPICE points and achieving state-of-the-art performance. We release the code at https: //github. com/DeepLearnXMU/KGR-4.

Authors

• Xin Liu
• Dayiheng Liu Alibaba DAMO Academy
• Baosong Yang
• Haibo Zhang
• Junwei Ding
• Wenqing Yao
• Weihua Luo Alibaba International Digital Commerce
• Haiying Zhang
• Jinsong Su School of Informatics, Xiamen University, China Shanghai Artificial Intelligence Laboratory, China Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, China

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