Towards Zero-resource Cross-lingual Entity Linking
This work addresses the challenge of entity linking for low-resource languages, which is incremental as it builds on existing methods but focuses on truly resource-scarce scenarios.
The paper tackles the problem of cross-lingual entity linking in low-resource languages by analyzing the impact of resource assumptions and proposing improvements to candidate generation and disambiguation, resulting in accuracy gains of 6-23% on four extremely low-resource languages.
Cross-lingual entity linking (XEL) grounds named entities in a source language to an English Knowledge Base (KB), such as Wikipedia. XEL is challenging for most languages because of limited availability of requisite resources. However, much previous work on XEL has been on simulated settings that actually use significant resources (e.g. source language Wikipedia, bilingual entity maps, multilingual embeddings) that are unavailable in truly low-resource languages. In this work, we first examine the effect of these resource assumptions and quantify how much the availability of these resource affects overall quality of existing XEL systems. Next, we propose three improvements to both entity candidate generation and disambiguation that make better use of the limited data we do have in resource-scarce scenarios. With experiments on four extremely low-resource languages, we show that our model results in gains of 6-23% in end-to-end linking accuracy.