Adversarial Learning for Multi-Lingual Entity Linking

AbstractEntity linking aims to identify mentions from the text and link them to a knowledge base. Further, Multi-lingual Entity Linking (MEL) is a more challenging task, where the language-specific mentions need to be linked to a multi-lingual knowledge base. To tackle the MEL task, we propose a novel model that employs the merit of adversarial learning and few-shot learning to generalize the learning ability across languages. Specifically, we first randomly select a fraction of language-agnostic unlabeled data as the language signal to construct the language discriminator. Based on it, we devise a simple and effective adversarial learning framework with two characteristic branches, including an entity classifier and a language discriminator with adversarial training. Experimental results on two benchmark datasets indicate the excellent performance in few-shot learning and the effectiveness of the proposed adversarial learning framework.