Learning Language-agnostic Entity Prototype for Zero-shot Cross-lingual Entity Linking
Haihong Yang, Zhongkai Hu, Yuchen Jiang, Boxing Chen, Huajun Chen
Following the recent trend of language model pre-training, we propose to learn entity prototypes for building a general-purpose entity linking system. Our model, Entity Prototype Network (EPN), is able to produce language-agnostic entity prototypes by simply reading entity description and transforms dissimilar surface forms in different languages to the neighborhood of the corresponding entity prototype in vector space.
We define a new Zero-shot Cross-lingual Entity Linking (ZXEL) task as testbed, validating the core assumption of learnable language-agnostic entity prototype. We also propose a simple yet effective auxiliary task termed Entity Identification which further improves our model to capture entity similarity including intra-entity similarity and inter-entity dissimilarity. We evaluate our model on the new task in classic and generalized setting.
Experiment results highlight the consistent improvement (>30% on average) of this approach over competitive baselines. Ablation study justifies the necessity of our model design and reveals the effect of the proposed auxiliary task. Data resources and out-of-the-box code will be publicly available after anonymous period.