no code implementations • NAACL 2022 • Yang Yan, Junda Ye, Zhongbao Zhang, LiWen Wang
As an essential component of task-oriented dialogue systems, slot filling requires enormous labeled training data in a certain domain.
no code implementations • 27 Feb 2023 • Daichi Guo, Guanting Dong, Dayuan Fu, Yuxiang Wu, Chen Zeng, Tingfeng Hui, LiWen Wang, Xuefeng Li, Zechen Wang, Keqing He, Xinyue Cui, Weiran Xu
In real dialogue scenarios, the existing slot filling model, which tends to memorize entity patterns, has a significantly reduced generalization facing Out-of-Vocabulary (OOV) problems.
no code implementations • 27 Feb 2023 • Guanting Dong, Zechen Wang, LiWen Wang, Daichi Guo, Dayuan Fu, Yuxiang Wu, Chen Zeng, Xuefeng Li, Tingfeng Hui, Keqing He, Xinyue Cui, QiXiang Gao, Weiran Xu
Specifically, we decouple class-specific prototypes and contextual semantic prototypes by two masking strategies to lead the model to focus on two different semantic information for inference.
no code implementations • COLING 2022 • Guanting Dong, Daichi Guo, LiWen Wang, Xuefeng Li, Zechen Wang, Chen Zeng, Keqing He, Jinzheng Zhao, Hao Lei, Xinyue Cui, Yi Huang, Junlan Feng, Weiran Xu
Most existing slot filling models tend to memorize inherent patterns of entities and corresponding contexts from training data.
no code implementations • 26 Apr 2022 • Xuefeng Li, Hao Lei, LiWen Wang, Guanting Dong, Jinzheng Zhao, Jiachi Liu, Weiran Xu, Chunyun Zhang
In this paper, we propose a robust contrastive alignment method to align text classification features of various domains in the same feature space by supervised contrastive learning.
1 code implementation • 8 Mar 2022 • LiWen Wang, Rumei Li, Yang Yan, Yuanmeng Yan, Sirui Wang, Wei Wu, Weiran Xu
Recently, prompt-based methods have achieved significant performance in few-shot learning scenarios by bridging the gap between language model pre-training and fine-tuning for downstream tasks.
1 code implementation • EMNLP 2021 • LiWen Wang, Xuefeng Li, Jiachi Liu, Keqing He, Yuanmeng Yan, Weiran Xu
Zero-shot cross-domain slot filling alleviates the data dependence in the case of data scarcity in the target domain, which has aroused extensive research.
1 code implementation • NAACL 2021 • LiWen Wang, Yuanmeng Yan, Keqing He, Yanan Wu, Weiran Xu
In this paper, we propose an adversarial disentangled debiasing model to dynamically decouple social bias attributes from the intermediate representations trained on the main task.