Divide, Conquer, and Combine: Mixture of Semantic-Independent Experts
for Zero-Shot Dialogue State Tracking

Divide, Conquer, and Combine: Mixture of Semantic-Independent Experts for Zero-Shot Dialogue State Tracking

1 June 2023

Liang Ding

Yibing Zhan

Zheng Lin

Papers citing "Divide, Conquer, and Combine: Mixture of Semantic-Independent Experts for Zero-Shot Dialogue State Tracking"

8 / 8 papers shown

Title
Distilled Transformers with Locally Enhanced Global Representations for Face Forgery Detection Yaning Zhang Qiufu Li Zitong Yu L. Shen ViT 45 3 0 31 Dec 2024
Diverse and Effective Synthetic Data Generation for Adaptable Zero-Shot Dialogue State Tracking James D. Finch Jinho D. Choi 29 0 0 21 May 2024
MoPE: Mixture of Prefix Experts for Zero-Shot Dialogue State Tracking Tianwen Tang Tong Zhu Haodong Liu Yin Bai Jia Cheng Wenliang Chen MoE 19 0 0 12 Apr 2024
Diversifying the Mixture-of-Experts Representation for Language Models with Orthogonal Optimizer Boan Liu Liang Ding Li Shen Keqin Peng Yu Cao Dazhao Cheng Dacheng Tao MoE 34 7 0 15 Oct 2023
Towards Making the Most of ChatGPT for Machine Translation Keqin Peng Liang Ding Qihuang Zhong Li Shen Xuebo Liu Min Zhang Y. Ouyang Dacheng Tao LRM 83 203 0 24 Mar 2023
Single-dataset Experts for Multi-dataset Question Answering Dan Friedman Ben Dodge Danqi Chen MoMe 121 26 0 28 Sep 2021
Slot Self-Attentive Dialogue State Tracking Fanghua Ye Jarana Manotumruksa Qiang Zhang Shenghui Li Emine Yilmaz 30 63 0 22 Jan 2021
Semantics Disentangling for Generalized Zero-Shot Learning Zhi Chen Yadan Luo Ruihong Qiu Sen Wang Zi Huang Jingjing Li Zheng-Wei Zhang 68 98 0 20 Jan 2021