CODE: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert Linking

2 code implementations • 14 Dec 2020 • Bo Chen, Jing Zhang, Xiaokang Zhang, Xiaobin Tang, Lingfan Cai, Hong Chen, Cuiping Li, Peng Zhang, Jie Tang

In this paper, we propose CODE, which first pre-trains an expert linking model by contrastive learning on AMiner such that it can capture the representation and matching patterns of experts without supervised signals, then it is fine-tuned between AMiner and external sources to enhance the models transferability in an adversarial manner.

Active Learning Contrastive Learning +2