Modern Entity Linking (EL) systems entrench a popularity bias, yet there is no dataset focusing on tail and emerging entities in languages other than English. We present Hansel, a new benchmark in Chinese that fills the vacancy of non-English few-shot and zero-shot EL challenges. The test set of Hansel is human annotated and reviewed, created with a novel method for collecting zero-shot EL datasets. It covers 10K diverse documents in news, social media posts and other web articles, with Wikidata as its target Knowledge Base. We demonstrate that the existing state-of-the-art EL system performs poorly on Hansel (R@1 of 36.6% on Few-Shot). We then establish a strong baseline that scores a R@1 of 46.2% on Few-Shot and 76.6% on Zero-Shot on our dataset. We also show that our baseline achieves competitive results on TAC-KBP2015 Chinese Entity Linking task.

翻译：现代实体链接系统普遍存在流行度偏差，但针对非英语语言中尾部及新兴实体的数据集尚属空白。我们提出Hansel，这是一个填补非英语少样本及零样本实体链接挑战空白的中文新基准。Hansel的测试集通过人工标注与审核构建，并采用创新方法收集零样本实体链接数据集。该数据集涵盖新闻、社交媒体帖子及其他网络文章中的10,000篇多样化文档，以Wikidata为目标知识库。实验表明，现有最优实体链接系统在Hansel上表现不佳（少样本R@1为36.6%）。我们进一步建立强基线模型，在该数据集上少样本及零样本的R@1分别达到46.2%和76.6%。同时，该基线在TAC-KBP2015中文实体链接任务中亦取得有竞争力的结果。