Search Results for author: Junlan Feng

Found 10 papers, 2 papers with code

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

no 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.

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.

Time Series Unsupervised Anomaly Detection

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

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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