Language-agnostic Zero-shot Entity Linking via Entity Prototype Network

Following the recent trend of language understanding, we introduce a novel approach for constructing a general-purpose entity linking system by learning entity prototypes. Our model, the Entity Prototype Network (EPN), generates language-agnostic entity prototypes by reading entity descriptions and aligning diverse surface forms in different languages to the corresponding entity prototype in a unified vector space. To validate our core assumption of learnable language-agnostic entity prototypes, we define a new Zero-shot Cross-lingual Entity Linking (ZXEL) task. Additionally, we propose a simple yet effective auxiliary task termed Entity Identification, which enhances our model’s ability to capture both intra-entity similarity and inter-entity dissimilarity. We evaluate our model on the new task in both classic and generalized settings. Experimental results demonstrate consistent improvements (exceeding 30% on average) over competitive baselines. An ablation study confirms the necessity of our model design and highlights the impact of the proposed auxiliary task.