Zhi Zhang


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CK-Transformer: Commonsense Knowledge Enhanced Transformers for Referring Expression Comprehension
Zhi Zhang | Helen Yannakoudakis | Xiantong Zhen | Ekaterina Shutova
Findings of the Association for Computational Linguistics: EACL 2023

The task of multimodal referring expression comprehension (REC), aiming at localizing an image region described by a natural language expression, has recently received increasing attention within the research comminity. In this paper, we specifically focus on referring expression comprehension with commonsense knowledge (KB-Ref), a task which typically requires reasoning beyond spatial, visual or semantic information. We propose a novel framework for Commonsense Knowledge Enhanced Transformers (CK-Transformer) which effectively integrates commonsense knowledge into the representations of objects in an image, facilitating identification of the target objects referred to by the expressions. We conduct extensive experiments on several benchmarks for the task of KB-Ref. Our results show that the proposed CK-Transformer achieves a new state of the art, with an absolute improvement of 3.14% accuracy over the existing state of the art.


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A Hybrid Approach of Deep Semantic Matching and Deep Rank for Context Aware Question Answer System
Shu-Yi Xie | Chia-Hao Chang | Zhi Zhang | Yang Mo | Lian-Xin Jiang | Yu-Sheng Huang | Jian-Ping Shen
Proceedings of the 31st Conference on Computational Linguistics and Speech Processing (ROCLING 2019)