no code implementations • 17 Nov 2023 • Ting Xu, Zhen Wu, Huiyun Yang, Xinyu Dai
We propose to explore ASTE in the cross-domain setting, which transfers knowledge from a resource-rich source domain to a resource-poor target domain, thereby alleviating the reliance on labeled data in the target domain.
1 code implementation • 27 May 2023 • Ting Xu, Huiyun Yang, Zhen Wu, Jiaze Chen, Fei Zhao, Xinyu Dai
In this paper, we introduce a new dataset, named DMASTE, which is manually annotated to better fit real-world scenarios by providing more diverse and realistic reviews for the task.
1 code implementation • ICLR 2022 • Huiyun Yang, Huadong Chen, Hao Zhou, Lei LI
Based on large-scale pre-trained multilingual representations, recent cross-lingual transfer methods have achieved impressive transfer performances.
no code implementations • IJCNLP 2019 • Huiyun Yang, Shu-Jian Huang, Xin-yu Dai, Jia-Jun Chen
In sequence labeling, previous domain adaptation methods focus on the adaptation from the source domain to the entire target domain without considering the diversity of individual target domain samples, which may lead to negative transfer results for certain samples.