  This paper describes our system for SemEval-2020 Task 4: Commonsense
Validation and Explanation (Wang et al., 2020). We propose a novel
Knowledge-enhanced Graph Attention Network (KEGAT) architecture for this task,
leveraging heterogeneous knowledge from both the structured knowledge base
(i.e. ConceptNet) and unstructured text to better improve the ability of a
machine in commonsense understanding. This model has a powerful commonsense
inference capability via utilizing suitable commonsense incorporation methods
and upgraded data augmentation techniques. Besides, an internal sharing
mechanism is cooperated to prohibit our model from insufficient and excessive
reasoning for commonsense. As a result, this model performs quite well in both
validation and explanation. For instance, it achieves state-of-the-art accuracy
in the subtask called Commonsense Explanation (Multi-Choice). We officially
name the system as ECNU-SenseMaker. Code is publicly available at
https://github.com/ECNU-ICA/ECNU-SenseMaker.
