Expert finding, a popular service provided by many online websites such as Expertise Finder, LinkedIn, and AMiner, is beneficial to seeking candidate qualifications, consultants, and collaborators. However, its quality is suffered from lack of ample sources of expert information. This paper employs AMiner as the basis with an aim at linking any external experts to the counterparts on AMiner. As it is infeasible to acquire sufficient linkages from arbitrary external sources, we explore the problem of zero-shot expert linking. 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. For evaluation, we first design two intrinsic tasks, author identification and paper clustering, to validate the representation and matching capability endowed by contrastive learning. Then the final external expert linking performance on two genres of external sources also implies the superiority of the adversarial fine-tuning method. Additionally, we show the online deployment of CODE, and continuously improve its online performance via active learning.
翻译:专家发现是许多在线网站,如专家发现者、LinkedIn和AMiner等提供的一种广受欢迎的服务,这种专家发现有助于寻求候选人资格、顾问和协作者,然而,其质量却因缺乏足够的专家信息来源而受到影响。本文将Aminer作为基础,目的是将任何外部专家与Aminer的对应人员联系起来。由于无法从任意的外部来源获得足够的联系,我们探讨了零点点专家联系的问题。在本文件中,我们建议CODE首先将专家通过对Aminer的对比性学习将模型联系起来,这样它就可以在没有监督信号的情况下捕捉专家的代表性和匹配模式,然后将Aminer和外部来源加以微调,以便以对抗的方式提高模型的可转移性。关于评价,我们首先设计两项内在任务,即作者识别和文件集中,以验证通过对比性学习获得的代表性和匹配能力。然后,将两种外部来源的业绩联系起来的最后外部专家也意味着对抗性微调方法的优越性。此外,我们展示CODE的在线部署,并通过在线学习不断改进其积极性。