Pingsheng Liu

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2021

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ECNU_ICA_1 SemEval-2021 Task 4: Leveraging Knowledge-enhanced Graph Attention Networks for Reading Comprehension of Abstract Meaning
Pingsheng Liu | Linlin Wang | Qian Zhao | Hao Chen | Yuxi Feng | Xin Lin | Liang He
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

This paper describes our system for SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning. To accomplish this task, we utilize the Knowledge-Enhanced Graph Attention Network (KEGAT) architecture with a novel semantic space transformation strategy. It leverages heterogeneous knowledge to learn adequate evidences, and seeks for an effective semantic space of abstract concepts to better improve the ability of a machine in understanding the abstract meaning of natural language. Experimental results show that our system achieves strong performance on this task in terms of both imperceptibility and nonspecificity.
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