Huimin Chen


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TaiChi: Improving the Robustness of NLP Models by Seeking Common Ground While Reserving Differences
Huimin Chen | Chengyu Wang | Yanhao Wang | Cen Chen | Yinggui Wang
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

Recent studies have shown that Pre-trained Language Models (PLMs) are vulnerable to adversarial examples, crafted by introducing human-imperceptible perturbations to clean examples to deceive the models. This vulnerability stems from the divergence in the data distributions of clean and adversarial examples. Therefore, addressing this issue involves teaching the model to diminish the differences between the two types of samples and to focus more on their similarities. To this end, we propose a novel approach named TaiChi that employs a Siamese network architecture. Specifically, it consists of two sub-networks sharing the same structure but trained on clean and adversarial samples, respectively, and uses a contrastive learning strategy to encourage the generation of similar language representations for both kinds of samples. Furthermore, it utilizes the Kullback-Leibler (KL) divergence loss to enhance the consistency in the predictive behavior of the two sub-networks. Extensive experiments across three widely used datasets demonstrate that TaiChi achieves superior trade-offs between robustness to adversarial attacks at token and character levels and accuracy on clean examples compared to previous defense methods. Our code and data are publicly available at


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Jiuge: A Human-Machine Collaborative Chinese Classical Poetry Generation System
Guo Zhipeng | Xiaoyuan Yi | Maosong Sun | Wenhao Li | Cheng Yang | Jiannan Liang | Huimin Chen | Yuhui Zhang | Ruoyu Li
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

Research on the automatic generation of poetry, the treasure of human culture, has lasted for decades. Most existing systems, however, are merely model-oriented, which input some user-specified keywords and directly complete the generation process in one pass, with little user participation. We believe that the machine, being a collaborator or an assistant, should not replace human beings in poetic creation. Therefore, we proposed Jiuge, a human-machine collaborative Chinese classical poetry generation system. Unlike previous systems, Jiuge allows users to revise the unsatisfied parts of a generated poem draft repeatedly. According to the revision, the poem will be dynamically updated and regenerated. After the revision and modification procedure, the user can write a satisfying poem together with Jiuge system collaboratively. Besides, Jiuge can accept multi-modal inputs, such as keywords, plain text or images. By exposing the options of poetry genres, styles and revision modes, Jiuge, acting as a professional assistant, allows constant and active participation of users in poetic creation.


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Neural Sentiment Classification with User and Product Attention
Huimin Chen | Maosong Sun | Cunchao Tu | Yankai Lin | Zhiyuan Liu
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing