1 code implementation • NAACL 2022 • Yanan Wu, Keqing He, Yuanmeng Yan, QiXiang Gao, Zhiyuan Zeng, Fujia Zheng, Lulu Zhao, Huixing Jiang, Wei Wu, Weiran Xu
Detecting Out-of-Domain (OOD) or unknown intents from user queries is essential in a task-oriented dialog system.
1 code implementation • ACL 2022 • Yutao Mou, Keqing He, Yanan Wu, Zhiyuan Zeng, Hong Xu, Huixing Jiang, Wei Wu, Weiran Xu
Discovering Out-of-Domain(OOD) intents is essential for developing new skills in a task-oriented dialogue system.
1 code implementation • Findings (NAACL) 2022 • Shusen Wang, Bin Duan, Yanan Wu, Yajing Xu
In this paper, we propose a novel method based on Instance Ranking and Label Calibration strategies (IRLC) to learn discriminative representations for open relation extraction.
1 code implementation • Findings (NAACL) 2022 • Shusen Wang, Bosen Zhang, Yajing Xu, Yanan Wu, Bo Xiao
Zero-shot relation extraction aims to identify novel relations which cannot be observed at the training stage.
1 code implementation • 15 Dec 2022 • Yanan Wu, Shuiqing Zhao, Shouliang Qi, Jie Feng, Haowen Pang, Runsheng Chang, Long Bai, Mengqi Li, Shuyue Xia, Wei Qian, Hongliang Ren
In the first stage, the total airway mask and CT images are provided to the subnetwork, and the intrapulmonary airway mask and corresponding CT scans to the subnetwork in the second stage.
1 code implementation • 19 Oct 2022 • Yutao Mou, Pei Wang, Keqing He, Yanan Wu, Jingang Wang, Wei Wu, Weiran Xu
Specifically, we design a K-nearest neighbor contrastive learning (KNCL) objective for representation learning and introduce a KNN-based scoring function for OOD detection.
1 code implementation • 17 Oct 2022 • Yutao Mou, Keqing He, Pei Wang, Yanan Wu, Jingang Wang, Wei Wu, Weiran Xu
For OOD clustering stage, we propose a KCC method to form compact clusters by mining true hard negative samples, which bridges the gap between clustering and representation learning.
no code implementations • 17 Oct 2022 • Yanan Wu, Zhiyuan Zeng, Keqing He, Yutao Mou, Pei Wang, Yuanmeng Yan, Weiran Xu
In this paper, we propose a simple but strong energy-based score function to detect OOD where the energy scores of OOD samples are higher than IND samples.
1 code implementation • COLING 2022 • Yanan Wu, Zhiyuan Zeng, Keqing He, Yutao Mou, Pei Wang, Weiran Xu
Out-of-Domain (OOD) detection is a key component in a task-oriented dialog system, which aims to identify whether a query falls outside the predefined supported intent set.
1 code implementation • COLING 2022 • Yutao Mou, Keqing He, Yanan Wu, Pei Wang, Jingang Wang, Wei Wu, Yi Huang, Junlan Feng, Weiran Xu
Traditional intent classification models are based on a pre-defined intent set and only recognize limited in-domain (IND) intent classes.
1 code implementation • NAACL 2022 • Lulu Zhao, Fujia Zheng, Weihao Zeng, Keqing He, Weiran Xu, Huixing Jiang, Wei Wu, Yanan Wu
The most advanced abstractive dialogue summarizers lack generalization ability on new domains and the existing researches for domain adaptation in summarization generally rely on large-scale pre-trainings.
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.
1 code implementation • ACL 2021 • Zhiyuan Zeng, Keqing He, Yuanmeng Yan, Zijun Liu, Yanan Wu, Hong Xu, Huixing Jiang, Weiran Xu
Detecting Out-of-Domain (OOD) or unknown intents from user queries is essential in a task-oriented dialog system.
1 code implementation • ACL 2021 • Yanan Wu, Zhiyuan Zeng, Keqing He, Hong Xu, Yuanmeng Yan, Huixing Jiang, Weiran Xu
Existing slot filling models can only recognize pre-defined in-domain slot types from a limited slot set.
no code implementations • 30 Apr 2021 • Yanan Wu, He Liu, Songhe Feng, Yi Jin, Gengyu Lyu, Zizhang Wu
Multi-Label Image Classification (MLIC) aims to predict a set of labels that present in an image.