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 • 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.
no code implementations • EMNLP 2020 • Yuanmeng Yan, Keqing He, Hong Xu, Sihong Liu, Fanyu Meng, Min Hu, Weiran Xu
Open-vocabulary slots, such as file name, album name, or schedule title, significantly degrade the performance of neural-based slot filling models since these slots can take on values from a virtually unlimited set and have no semantic restriction nor a length limit.
no code implementations • Findings (EMNLP) 2021 • Yuejie Lei, Fujia Zheng, Yuanmeng Yan, Keqing He, Weiran Xu
Although abstractive summarization models have achieved impressive results on document summarization tasks, their performance on dialogue modeling is much less satisfactory due to the crude and straight methods for dialogue encoding.
Abstractive Dialogue Summarization
Abstractive Text Summarization
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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.
1 code implementation • 21 Dec 2022 • Jiakang Xu, Wolfgang Mayer, Hongyu Zhang, Keqing He, Zaiwen Feng
Therefore, an automatic approach for learning the semantics of a data source is desirable.
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 • Weihao Zeng, Keqing He, Zechen Wang, Dayuan Fu, Guanting Dong, Ruotong Geng, Pei Wang, Jingang Wang, Chaobo Sun, Wei Wu, Weiran Xu
Recent advances in neural approaches greatly improve task-oriented dialogue (TOD) systems which assist users to accomplish their goals.
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.
no code implementations • 31 Aug 2022 • Keqing He, Jingang Wang, Chaobo Sun, Wei Wu
In this paper, we propose a novel unified knowledge prompt pre-training framework, UFA (\textbf{U}nified Model \textbf{F}or \textbf{A}ll Tasks), for customer service dialogues.
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.
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.
no code implementations • 25 Oct 2021 • Lulu Zhao, Fujia Zheng, Keqing He, Weihao Zeng, Yuejie Lei, Huixing Jiang, Wei Wu, Weiran Xu, Jun Guo, Fanyu Meng
Previous dialogue summarization datasets mainly focus on open-domain chitchat dialogues, while summarization datasets for the broadly used task-oriented dialogue haven't been explored yet.
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 • Zhiyuan Zeng, Keqing He, Yuanmeng Yan, Hong Xu, Weiran Xu
Detecting out-of-domain (OOD) intents is crucial for the deployed task-oriented dialogue system.
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 • COLING 2020 • Keqing He, Jinchao Zhang, Yuanmeng Yan, Weiran Xu, Cheng Niu, Jie zhou
In this paper, we propose a Contrastive Zero-Shot Learning with Adversarial Attack (CZSL-Adv) method for the cross-domain slot filling.
no code implementations • COLING 2020 • Hong Xu, Keqing He, Yuanmeng Yan, Sihong Liu, Zijun Liu, Weiran Xu
Detecting out-of-domain (OOD) input intents is critical in the task-oriented dialog system.
no code implementations • COLING 2020 • Keqing He, Shuyu Lei, Yushu Yang, Huixing Jiang, Zhongyuan Wang
Slot filling and intent detection are two major tasks for spoken language understanding.
no code implementations • ACL 2020 • Keqing He, Yuanmeng Yan, Weiran Xu
Neural-based context-aware models for slot tagging have achieved state-of-the-art performance.
1 code implementation • 25 Apr 2020 • Liwei Huang, Yutao Ma, Yanbo Liu, Keqing He
In particular, the DAN-SNR makes use of the self-attention mechanism instead of the architecture of recurrent neural networks to model sequential influence and social influence in a unified manner.