Scaling Intermediate-Task Data for Robust Zero-Shot Cross-Lingual Transfer in XTREME-R

Abstract

Abstract: Abstract In cross-Lingual Named Entity Disambiguation (XNED) the task is to link Named Entity mentions in text in some native language to English entities in a knowledge graph. XNED systems usually require training data for each native language, limiting their application for low resource languages with small amounts of training data. Prior work have proposed so-called zero-shot transfer systems which are only trained in English training data, but required native prior probabilities of entities with respect to mentions, which had to be estimated from native training examples, limiting their pr

Research Question

What is the impact of scaling intermediate-task training data volume on the robustness of zero-shot cross-lingual transfer for low-resource languages in the XTREME-R evaluation?

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