Search Results for author: Junlan Feng

Found 29 papers, 12 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.

Dialogue State Tracking Task-Oriented Dialogue Systems

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.

Pretrained Language Models

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 Auxiliary Learning +3

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

1 code implementation13 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.

Pretrained Language Models

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

1 code implementation9 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.

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 +1

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.


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

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

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 +1

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