Qian Zhao


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多模态表述视域下的小学数学课堂语言计量初探(A preliminary study of language measurement in elementary school mathematics classrooms from the perspective of multimodal representation)
Zezhi Zheng (郑泽芝) | Qian Zhao (赵骞)
Proceedings of the 20th Chinese National Conference on Computational Linguistics


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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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ECNU-SenseMaker at SemEval-2020 Task 4: Leveraging Heterogeneous Knowledge Resources for Commonsense Validation and Explanation
Qian Zhao | Siyu Tao | Jie Zhou | Linlin Wang | Xin Lin | Liang He
Proceedings of the Fourteenth Workshop on Semantic Evaluation

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.