Jason Zhang


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Using Deep Mixture-of-Experts to Detect Word Meaning Shift for TempoWiC
Ze Chen | Kangxu Wang | Zijian Cai | Jiewen Zheng | Jiarong He | Max Gao | Jason Zhang
Proceedings of the The First Workshop on Ever Evolving NLP (EvoNLP)

This paper mainly describes the dma submission to the TempoWiC task, which achieves a macro-F1 score of 77.05% and attains the first place in this task. We first explore the impact of different pre-trained language models. Then we adopt data cleaning, data augmentation, and adversarial training strategies to enhance the model generalization and robustness. For further improvement, we integrate POS information and word semantic representation using a Mixture-of-Experts (MoE) approach. The experimental results show that MoE can overcome the feature overuse issue and combine the context, POS, and word semantic features well. Additionally, we use a model ensemble method for the final prediction, which has been proven effective by many research works.


Medstract: creating large-scale information servers from biomedical texts
James Pustejovsky | José Castaño | Roser Saurí | Jason Zhang | Wei Luo
Proceedings of the ACL-02 Workshop on Natural Language Processing in the Biomedical Domain