论文标题
ECNU-SEMEMAKER在SEMEVAL-2020任务4：利用异构知识资源进行常识验证和解释
ECNU-SenseMaker at SemEval-2020 Task 4: Leveraging Heterogeneous Knowledge Resources for Commonsense Validation and Explanation
论文作者
论文摘要
本文描述了我们针对2020年Semeval-2020任务的系统4：常识验证和解释（Wang等，2020）。我们为此任务提出了一个新颖的知识增强图表网络（KEGAT）体系结构，从结构化知识库（即概念网）和非结构化文本中利用异质知识，以更好地提高机器在常识中的能力。该模型通过利用合适的常识融合方法和升级的数据增强技术具有强大的常识推理能力。此外，还配合了内部共享机制，以禁止我们的模型不足和过度推理常识。结果，该模型在验证和解释中都表现良好。例如，它在称为Commensense解释（多选择性）的子任务中实现了最新的准确性。我们正式将该系统命名为ECNU-SEMEMAKER。代码可在https://github.com/ecnu-ica/ecnu-sensemaker上公开获取。
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.
