Papers by Zhenfei Yang
MoKA:Parameter Efficiency Fine-Tuning via Mixture of Kronecker Product Adaption (2025.coling-main)
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| Challenge: | Low-Rank Adaptation (LoRA) is one of the most popular PEFT methods . low-rank update mechanism of LoRA somewhat limits its ability to approximate full-parameter fine-tuning during training process. |
| Approach: | They propose a parameter-efficient fine-tuning framework that combines Kronecker product with the Mixture-of-Experts method to achieve parameter efficiency and better model performance. |
| Outcome: | The proposed framework outperforms existing methods on the GLUE benchmark and instruction tuning tasks for large language models. |
KC-ISA: An Implicit Sentiment Analysis Model Combining Knowledge Enhancement and Context Features (2022.coling-1)
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| Challenge: | Existing research results on explicit sentiment analysis are limited . implicit sentiment analysis is a process of analyzing text based on whether it contains explicit sentiment words. |
| Approach: | They propose a model that integrates external knowledge and contextual features . they use a knowledge graph to supplement implicit sentiment expression . |
| Outcome: | The proposed model can achieve better results on the SMP2019 implicit sentiment analysis dataset. |
BERT-BC: A Unified Alignment and Interaction Model over Hierarchical BERT for Response Selection (2024.lrec-main)
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| Challenge: | Recent performance boosting for dialogue response selection task achieved by Cross-Encoder based models is limited and the learned models have poor generalization capability in realistic scenarios. |
| Approach: | They propose a model that combines the representation-based Bi-Encoder and interaction-based Cross-Encoding to achieve better semantic representation. |
| Outcome: | The proposed model can achieve state-of-the-art performance on three benchmark datasets for multi-turn response selection. |