Xi Zhang


Inconsistency Matters: A Knowledge-guided Dual-inconsistency Network for Multi-modal Rumor Detection
Mengzhu Sun | Xi Zhang | Jianqiang Ma | Yazheng Liu
Findings of the Association for Computational Linguistics: EMNLP 2021

Rumor spreaders are increasingly utilizing multimedia content to attract the attention and trust of news consumers. Though a set of rumor detection models have exploited the multi-modal data, they seldom consider the inconsistent relationships among images and texts. Moreover, they also fail to find a powerful way to spot the inconsistency information among the post contents and background knowledge. Motivated by the intuition that rumors are more likely to have inconsistency information in semantics, a novel Knowledge-guided Dual-inconsistency network is proposed to detect rumors with multimedia contents. It can capture the inconsistent semantics at the cross-modal level and the content-knowledge level in one unified framework. Extensive experiments on two public real-world datasets demonstrate that our proposal can outperform the state-of-the-art baselines.


Aicyber at SemEval-2016 Task 4: i-vector based sentence representation
Steven Du | Xi Zhang
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)