现实世界对话式实体链接需要的不仅仅是零样本
Real World Conversational Entity Linking Requires More Than Zeroshots
AI总结：
本文针对对话式实体链接在领域特定、长尾知识库和缺少标注数据条件下的零样本泛化问题，构建了基于Fandom和Reddit的评估场景与数据集，发现现有零样本模型性能显著下降，并呼吁设计适应有限资源的新评估与建模方法。
摘要：
实体链接（EL）在对话中的实际应用面临显著挑战，主要原因是缺乏带有实体标注的对话数据集，以及包含领域特定、长尾实体的知识库（KB）较为稀疏。我们设计了针对性的评估场景，以衡量资源受限条件下实体链接模型的效果。我们的评估使用了两个知识库：Fandom，用于体现现实世界实体链接的复杂性，以及广泛使用的 Wikipedia。首先，我们利用 Fandom 和一个基于 Reddit 上关于 Fandom 实体讨论而构建的新型零样本对话式实体链接数据集，评估实体链接模型泛化到新的陌生知识库的能力。然后，我们评估实体链接模型在未经事先训练的情况下对对话场景的适应性。我们的结果表明，当前的零样本实体链接模型在未经过事先训练就被引入新的、领域特定的知识库时表现不佳，性能显著下降。我们的发现揭示，以往的评估方法不足以捕捉零样本实体链接在现实世界中的复杂性，这凸显了需要新的方法来设计和评估对话式实体链接模型，以适应有限资源。本研究中提出的评估设置和数据集已公开发布。
英文摘要：
Entity linking (EL) in conversations faces notable challenges in practical applications, primarily due to the scarcity of entity-annotated conversational datasets and sparse knowledge bases (KB) containing domain-specific, long-tail entities. We designed targeted evaluation scenarios to measure the efficacy of EL models under resource constraints. Our evaluation employs two KBs: Fandom, exemplifying real-world EL complexities, and the widely used Wikipedia. First, we assess EL models' ability to generalize to a new unfamiliar KB using Fandom and a novel zero-shot conversational entity linking dataset that we curated based on Reddit discussions on Fandom entities. We then evaluate the adaptability of EL models to conversational settings without prior training. Our results indicate that current zero-shot EL models falter when introduced to new, domain-specific KBs without prior training, significantly dropping in performance. Our findings reveal that previous evaluation approaches fall short of capturing real-world complexities for zero-shot EL, highlighting the necessity for new approaches to design and assess conversational EL models to adapt to limited resources. The evaluation setup and the dataset proposed in this research are made publicly available.
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