Search Results for author: Keqing He

Found 28 papers, 15 papers with code

Adversarial Semantic Decoupling for Recognizing Open-Vocabulary Slots

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.

slot-filling Slot Filling

A Finer-grain Universal Dialogue Semantic Structures based Model For Abstractive Dialogue Summarization

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 +1

Revisit Out-Of-Vocabulary Problem for Slot Filling: A Unified Contrastive Frameword with Multi-level Data Augmentations

no code implementations27 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.

Contrastive Learning slot-filling +1

A Prototypical Semantic Decoupling Method via Joint Contrastive Learning for Few-Shot Name Entity Recognition

no code implementations27 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.

Contrastive Learning few-shot-ner +4

UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood Learning

1 code implementation19 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.

Contrastive Learning Out of Distribution (OOD) Detection +2

Semi-Supervised Knowledge-Grounded Pre-training for Task-Oriented Dialog Systems

1 code implementation17 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.

Watch the Neighbors: A Unified K-Nearest Neighbor Contrastive Learning Framework for OOD Intent Discovery

1 code implementation17 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.

Contrastive Learning Intent Discovery +2

Disentangling Confidence Score Distribution for Out-of-Domain Intent Detection with Energy-Based Learning

no code implementations17 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.

Intent Detection Out of Distribution (OOD) Detection

Distribution Calibration for Out-of-Domain Detection with Bayesian Approximation

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.

Out of Distribution (OOD) Detection

Unified Knowledge Prompt Pre-training for Customer Service Dialogues

no code implementations31 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.

Natural Language Understanding Text Generation

Domain-Oriented Prefix-Tuning: Towards Efficient and Generalizable Fine-tuning for Zero-Shot Dialogue Summarization

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.

Domain Adaptation

TODSum: Task-Oriented Dialogue Summarization with State Tracking

no code implementations25 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.

Dynamically Disentangling Social Bias from Task-Oriented Representations with Adversarial Attack

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.

Adversarial Attack Representation Learning

DAN-SNR: A Deep Attentive Network for Social-Aware Next Point-of-Interest Recommendation

1 code implementation25 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.

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