Search Results for author: Junlan Feng

Found 43 papers, 19 papers with code

Counterfactual Matters: Intrinsic Probing For Dialogue State Tracking

no code implementations EANCS 2021 Yi Huang, Junlan Feng, Xiaoting Wu, Xiaoyu Du

Our findings are: the performance variance of generative DSTs is not only due to the model structure itself, but can be attributed to the distribution of cross-domain values.

counterfactual Dialogue State Tracking +1

Plugin Speech Enhancement: A Universal Speech Enhancement Framework Inspired by Dynamic Neural Network

no code implementations20 Feb 2024 Yanan Chen, Zihao Cui, Yingying Gao, Junlan Feng, Chao Deng, Shilei Zhang

In this study, we present a novel weighting prediction approach, which explicitly learns the task relationships from downstream training information to address the core challenge of universal speech enhancement.

Data Augmentation Speech Enhancement

Depth Map Denoising Network and Lightweight Fusion Network for Enhanced 3D Face Recognition

no code implementations1 Jan 2024 Ruizhuo Xu, Ke Wang, Chao Deng, Mei Wang, Xi Chen, Wenhui Huang, Junlan Feng, Weihong Deng

With the increasing availability of consumer depth sensors, 3D face recognition (FR) has attracted more and more attention.

Denoising Face Recognition

Prompt Pool based Class-Incremental Continual Learning for Dialog State Tracking

1 code implementation17 Nov 2023 Hong Liu, Yucheng Cai, Yuan Zhou, Zhijian Ou, Yi Huang, Junlan Feng

Inspired by the recently emerging prompt tuning method that performs well on dialog systems, we propose to use the prompt pool method, where we maintain a pool of key-value paired prompts and select prompts from the pool according to the distance between the dialog history and the prompt keys.

Continual Learning dialog state tracking

GenDistiller: Distilling Pre-trained Language Models based on Generative Models

no code implementations20 Oct 2023 Yingying Gao, Shilei Zhang, Zihao Cui, Yanhan Xu, Chao Deng, Junlan Feng

Self-supervised pre-trained models such as HuBERT and WavLM leverage unlabeled speech data for representation learning and offer significantly improve for numerous downstream tasks.

Knowledge Distillation Language Modelling +1

Fine-grained Recognition with Learnable Semantic Data Augmentation

1 code implementation1 Sep 2023 Yifan Pu, Yizeng Han, Yulin Wang, Junlan Feng, Chao Deng, Gao Huang

Since images belonging to the same meta-category usually share similar visual appearances, mining discriminative visual cues is the key to distinguishing fine-grained categories.

Data Augmentation Fine-Grained Image Recognition +2

Dynamic Perceiver for Efficient Visual Recognition

1 code implementation ICCV 2023 Yizeng Han, Dongchen Han, Zeyu Liu, Yulin Wang, Xuran Pan, Yifan Pu, Chao Deng, Junlan Feng, Shiji Song, Gao Huang

Early exits are placed exclusively within the classification branch, thus eliminating the need for linear separability in low-level features.

Action Recognition Classification +4

MPPN: Multi-Resolution Periodic Pattern Network For Long-Term Time Series Forecasting

no code implementations12 Jun 2023 Xing Wang, Zhendong Wang, Kexin Yang, Junlan Feng, Zhiyan Song, Chao Deng, Lin Zhu

To capture the intrinsic patterns of time series, we propose a novel deep learning network architecture, named Multi-resolution Periodic Pattern Network (MPPN), for long-term series forecasting.

Time Series Time Series Forecasting

Healing Unsafe Dialogue Responses with Weak Supervision Signals

1 code implementation25 May 2023 Zi Liang, Pinghui Wang, Ruofei Zhang, Shuo Zhang, Xiaofan Ye Yi Huang, Junlan Feng

Recent years have seen increasing concerns about the unsafe response generation of large-scale dialogue systems, where agents will learn offensive or biased behaviors from the real-world corpus.

Pseudo Label Response Generation

Knowledge-Retrieval Task-Oriented Dialog Systems with Semi-Supervision

1 code implementation22 May 2023 Yucheng Cai, Hong Liu, Zhijian Ou, Yi Huang, Junlan Feng

Most existing task-oriented dialog (TOD) systems track dialog states in terms of slots and values and use them to query a database to get relevant knowledge to generate responses.

Question Answering Retrieval

ESCL: Equivariant Self-Contrastive Learning for Sentence Representations

no code implementations9 Mar 2023 Jie Liu, Yixuan Liu, Xue Han, Chao Deng, Junlan Feng

Previous contrastive learning methods for sentence representations often focus on insensitive transformations to produce positive pairs, but neglect the role of sensitive transformations that are harmful to semantic representations.

Contrastive Learning Multi-Task Learning +2

Multi-Action Dialog Policy Learning from Logged User Feedback

no code implementations27 Feb 2023 Shuo Zhang, Junzhou Zhao, Pinghui Wang, Tianxiang Wang, Zi Liang, Jing Tao, Yi Huang, Junlan Feng

To cope with this problem, we explore to improve multi-action dialog policy learning with explicit and implicit turn-level user feedback received for historical predictions (i. e., logged user feedback) that are cost-efficient to collect and faithful to real-world scenarios.

Federated Learning over Coupled Graphs

no code implementations26 Jan 2023 Runze Lei, Pinghui Wang, Junzhou Zhao, Lin Lan, Jing Tao, Chao Deng, Junlan Feng, Xidian Wang, Xiaohong Guan

In this work, we propose a novel FL framework for graph data, FedCog, to efficiently handle coupled graphs that are a kind of distributed graph data, but widely exist in a variety of real-world applications such as mobile carriers' communication networks and banks' transaction networks.

Federated Learning Node Classification

A Generative User Simulator with GPT-based Architecture and Goal State Tracking for Reinforced Multi-Domain Dialog Systems

1 code implementation17 Oct 2022 Hong Liu, Yucheng Cai, Zhijian Ou, Yi Huang, Junlan Feng

Second, an important ingredient in a US is that the user goal can be effectively incorporated and tracked; but how to flexibly integrate goal state tracking and develop an end-to-end trainable US for multi-domains has remained to be a challenge.

Reinforcement Learning (RL)

Jointly Reinforced User Simulator and Task-oriented Dialog System with Simplified Generative Architecture

no code implementations13 Oct 2022 Hong Liu, Zhijian Ou, Yi Huang, Junlan Feng

Recently, there has been progress in supervised funetuning pretrained GPT-2 to build end-to-end task-oriented dialog (TOD) systems.

Information Extraction and Human-Robot Dialogue towards Real-life Tasks: A Baseline Study with the MobileCS Dataset

1 code implementation27 Sep 2022 Hong Liu, Hao Peng, Zhijian Ou, Juanzi Li, Yi Huang, Junlan Feng

Recently, there have merged a class of task-oriented dialogue (TOD) datasets collected through Wizard-of-Oz simulated games.

Advancing Semi-Supervised Task Oriented Dialog Systems by JSA Learning of Discrete Latent Variable Models

1 code implementation SIGDIAL (ACL) 2022 Yucheng Cai, Hong Liu, Zhijian Ou, Yi Huang, Junlan Feng

In this paper, we propose to apply JSA to semi-supervised learning of the latent state TOD models, which is referred to as JSA-TOD.

A Challenge on Semi-Supervised and Reinforced Task-Oriented Dialog Systems

1 code implementation6 Jul 2022 Zhijian Ou, Junlan Feng, Juanzi Li, Yakun Li, Hong Liu, Hao Peng, Yi Huang, Jiangjiang Zhao

A challenge on Semi-Supervised and Reinforced Task-Oriented Dialog Systems, Co-located with EMNLP2022 SereTOD Workshop.

Meta Auxiliary Learning for Low-resource Spoken Language Understanding

no code implementations26 Jun 2022 Yingying Gao, Junlan Feng, Chao Deng, Shilei Zhang

Spoken language understanding (SLU) treats automatic speech recognition (ASR) and natural language understanding (NLU) as a unified task and usually suffers from data scarcity.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +4

Curriculum-Based Self-Training Makes Better Few-Shot Learners for Data-to-Text Generation

1 code implementation6 Jun 2022 Pei Ke, Haozhe Ji, Zhenyu Yang, Yi Huang, Junlan Feng, Xiaoyan Zhu, Minlie Huang

Despite the success of text-to-text pre-trained models in various natural language generation (NLG) tasks, the generation performance is largely restricted by the number of labeled data in downstream tasks, particularly in data-to-text generation tasks.

Data-to-Text Generation Unsupervised Pre-training

"Think Before You Speak": Improving Multi-Action Dialog Policy by Planning Single-Action Dialogs

1 code implementation25 Apr 2022 Shuo Zhang, Junzhou Zhao, Pinghui Wang, Yu Li, Yi Huang, Junlan Feng

Multi-action dialog policy (MADP), which generates multiple atomic dialog actions per turn, has been widely applied in task-oriented dialog systems to provide expressive and efficient system responses.

Multi-Task Learning

Network Topology Optimization via Deep Reinforcement Learning

no code implementations19 Apr 2022 Zhuoran Li, Xing Wang, Ling Pan, Lin Zhu, Zhendong Wang, Junlan Feng, Chao Deng, Longbo Huang

A2C-GS consists of three novel components, including a verifier to validate the correctness of a generated network topology, a graph neural network (GNN) to efficiently approximate topology rating, and a DRL actor layer to conduct a topology search.

Management reinforcement-learning +1

Building Markovian Generative Architectures over Pretrained LM Backbones for Efficient Task-Oriented Dialog Systems

2 code implementations13 Apr 2022 Hong Liu, Yucheng Cai, Zhijian Ou, Yi Huang, Junlan Feng

Recently, Transformer based pretrained language models (PLMs), such as GPT2 and T5, have been leveraged to build generative task-oriented dialog (TOD) systems.

Multiple Confidence Gates For Joint Training Of SE And ASR

no code implementations1 Apr 2022 Tianrui Wang, Weibin Zhu, Yingying Gao, Junlan Feng, Shilei Zhang

Joint training of speech enhancement model (SE) and speech recognition model (ASR) is a common solution for robust ASR in noisy environments.

Robust Speech Recognition Speech Enhancement +1

Harmonic gated compensation network plus for ICASSP 2022 DNS CHALLENGE

no code implementations25 Feb 2022 Tianrui Wang, Weibin Zhu, Yingying Gao, Yanan Chen, Junlan Feng, Shilei Zhang

Therefore, we previously proposed a harmonic gated compensation network (HGCN) to predict the full harmonic locations based on the unmasked harmonics and process the result of a coarse enhancement module to recover the masked harmonics.

HGCN: Harmonic gated compensation network for speech enhancement

1 code implementation30 Jan 2022 Tianrui Wang, Weibin Zhu, Yingying Gao, Junlan Feng, Shilei Zhang

Mask processing in the time-frequency (T-F) domain through the neural network has been one of the mainstreams for single-channel speech enhancement.

Action Detection Activity Detection +1

Adaptive Multi-receptive Field Spatial-Temporal Graph Convolutional Network for Traffic Forecasting

no code implementations1 Nov 2021 Xing Wang, Juan Zhao, Lin Zhu, Xu Zhou, Zhao Li, Junlan Feng, Chao Deng, Yong Zhang

AMF-STGCN extends GCN by (1) jointly modeling the complex spatial-temporal dependencies in mobile networks, (2) applying attention mechanisms to capture various Receptive Fields of heterogeneous base stations, and (3) introducing an extra decoder based on a fully connected deep network to conquer the error propagation challenge with multi-step forecasting.

Variational Latent-State GPT for Semi-Supervised Task-Oriented Dialog Systems

2 code implementations9 Sep 2021 Hong Liu, Yucheng Cai, Zhenru Lin, Zhijian Ou, Yi Huang, Junlan Feng

In this paper, we propose Variational Latent-State GPT model (VLS-GPT), which is the first to combine the strengths of the two approaches.

Adaptive Spatial-Temporal Inception Graph Convolutional Networks for Multi-step Spatial-Temporal Network Data Forecasting

no code implementations1 Jan 2021 Xing Wang, Lin Zhu, Juan Zhao, Zhou Xu, Zhao Li, Junlan Feng, Chao Deng

Spatial-temporal data forecasting is of great importance for industries such as telecom network operation and transportation management.

Management

GenAD: General Representations of Multivariate Time Series for Anomaly Detection

no code implementations1 Jan 2021 Xiaolei Hua, Su Wang, Lin Zhu, Dong Zhou, Junlan Feng, Yiting Wang, Chao Deng, Shuo Wang, Mingtao Mei

However, due to complex correlations and various temporal patterns of large-scale multivariate time series, a general unsupervised anomaly detection model with higher F1-score and Timeliness remains a challenging task.

Management Time Series +2

Learning to Check Contract Inconsistencies

1 code implementation15 Dec 2020 Shuo Zhang, Junzhou Zhao, Pinghui Wang, Nuo Xu, Yang Yang, Yiting Liu, Yi Huang, Junlan Feng

This will result in the issue of contract inconsistencies, which may severely impair the legal validity of the contract.

A Probabilistic End-To-End Task-Oriented Dialog Model with Latent Belief States towards Semi-Supervised Learning

1 code implementation EMNLP 2020 Yichi Zhang, Zhijian Ou, Huixin Wang, Junlan Feng

In this paper we aim at alleviating the reliance on belief state labels in building end-to-end dialog systems, by leveraging unlabeled dialog data towards semi-supervised learning.

End-To-End Dialogue Modelling

Neural CRF transducers for sequence labeling

no code implementations4 Nov 2018 Kai Hu, Zhijian Ou, Min Hu, Junlan Feng

Conditional random fields (CRFs) have been shown to be one of the most successful approaches to sequence labeling.

Chunking NER +2

Elastic CRFs for Open-ontology Slot Filling

no code implementations4 Nov 2018 Yinpei Dai, Yichi Zhang, Zhijian Ou, Yanmeng Wang, Junlan Feng

Second, the one-hot encoding of slot labels ignores the semantic meanings and relations for slots, which are implicit in their natural language descriptions.

slot-filling Slot Filling

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