most citedEnhancing Cross-lingual Transfer by Manifold Mixup

23 citations · 48 across the 8 of their papers we have counts for

collaborators

7 papers

cs.CL20236 cited

On Pre-trained Language Models for Antibody

Danqing Wang, Fei Ye, Hao Zhou

Antibodies are vital proteins offering robust protection for the human body from pathogens. The development of general protein and antibody-specific pre-trained language models bot…

cs.LG20235 cited

Integrating Local Real Data with Global Gradient Prototypes for Classifier Re-Balancing in Federated Long-Tailed Learning

Wenkai Yang, Deli Chen, Hao Zhou +3

Federated Learning (FL) has become a popular distributed learning paradigm that involves multiple clients training a global model collaboratively in a data privacy-preserving manne…

cs.CL20221 cited

PARAGEN : A Parallel Generation Toolkit

Jiangtao Feng, Yi Zhou, Jun Zhang +7

PARAGEN is a PyTorch-based NLP toolkit for further development on parallel generation. PARAGEN provides thirteen types of customizable plugins, helping users to experiment quickly…

cs.CL2022

Manual-Guided Dialogue for Flexible Conversational Agents

Ryuichi Takanobu, Hao Zhou, Yankai Lin +3

How to build and use dialogue data efficiently, and how to deploy models in different domains at scale can be two critical issues in building a task-oriented dialogue system. In th…

cs.CL202223 cited

Enhancing Cross-lingual Transfer by Manifold Mixup

Huiyun Yang, Huadong Chen, Hao Zhou +1

Based on large-scale pre-trained multilingual representations, recent cross-lingual transfer methods have achieved impressive transfer performances. However, the performance of tar…

cs.CL20221 cited

-GLAT: Glancing at Latent Variables for Parallel Text Generation

Yu Bao, Hao Zhou, Shujian Huang +5

Recently, parallel text generation has received widespread attention due to its success in generation efficiency. Although many advanced techniques are proposed to improve its gene…