Fan Xu


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Leveraging Contrastive Learning and Knowledge Distillation for Incomplete Modality Rumor Detection
Fan Xu | Pinyun Fu | Qi Huang | Bowei Zou | AiTi Aw | Mingwen Wang
Findings of the Association for Computational Linguistics: EMNLP 2023

Rumors spread rapidly through online social microblogs at a relatively low cost, causing substantial economic losses and negative consequences in our daily lives. Existing rumor detection models often neglect the underlying semantic coherence between text and image components in multimodal posts, as well as the challenges posed by incomplete modalities in single modal posts, such as missing text or images. This paper presents CLKD-IMRD, a novel framework for Incomplete Modality Rumor Detection. CLKD-IMRD employs Contrastive Learning and Knowledge Distillation to capture the semantic consistency between text and image pairs, while also enhancing model generalization to incomplete modalities within individual posts. Extensive experimental results demonstrate that our CLKD-IMRD outperforms state-of-the-art methods on two English and two Chinese benchmark datasets for rumor detection in social media.


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“细粒度英汉机器翻译错误分析语料库”的构建与思考(Construction of Fine-Grained Error Analysis Corpus of English-Chinese Machine Translation and Its Implications)
Bailian Qiu (裘白莲) | Mingwen Wang (王明文) | Maoxi Li (李茂西) | Cong Chen (陈聪) | Fan Xu (徐凡)
Proceedings of the 19th Chinese National Conference on Computational Linguistics



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Building Parallel Monolingual Gan Chinese Dialects Corpus
Fan Xu | Mingwen Wang | Maoxi Li
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


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Building Monolingual Word Alignment Corpus for the Greater China Region
Fan Xu | Xiongfei Xu | Mingwen Wang | Maoxi Li
Proceedings of the Joint Workshop on Language Technology for Closely Related Languages, Varieties and Dialects


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A Unified Framework for Discourse Argument Identification via Shallow Semantic Parsing
Fan Xu | Qiaoming Zhu | Guodong Zhou
Proceedings of COLING 2012: Posters