To enhance the ability to find credible evidence in news articles, we propose a novel task of expert recommendation, which aims to identify trustworthy experts on a specific news topic. To achieve the aim, we describe the construction of a novel NewsQuote dataset consisting of 24,031 quote-speaker pairs that appeared on a COVID-19 news corpus. We demonstrate an automatic pipeline for speaker and quote extraction via a BERT-based Question Answering model. Then, we formulate expert recommendations as document retrieval task by retrieving relevant quotes first as an intermediate step for expert identification, and expert retrieval by directly retrieving sources based on the probability of a query conditional on a candidate expert. Experimental results on NewsQuote show that document retrieval is more effective in identifying relevant experts for a given news topic compared to expert retrieval

翻译：为增强从新闻文章中获取可信证据的能力，我们提出了一种新颖的专家推荐任务，旨在识别特定新闻主题中的可信专家。为此，我们描述了一个新型数据集NewsQuote的构建过程，该数据集包含24,031个出现在COVID-19新闻语料库中的引用-说话者对。我们展示了通过基于BERT的问答模型实现说话者与引文自动提取的流水线。随后，我们将专家推荐问题形式化为文档检索任务：首先通过检索相关引文作为识别专家的中间步骤，再通过直接基于查询条件概率在候选专家中检索源信息来进行专家检索。在NewsQuote上的实验结果表明，与直接专家检索相比，文档检索在识别给定新闻主题的相关专家方面更为有效。