GeoMLAMA: Geo-Diverse Commonsense Probing on Multilingual Pre-Trained Language Models

Understand

Recent work has shown that Pre-trained Language Models (PLMs) store the relational knowledge learned from data and utilize it for performing downstream tasks.

• However, commonsense knowledge across different regions may vary.
• For instance, the color of bridal dress is white in American weddings whereas it is red in Chinese weddings.
• In this paper, we introduce a benchmark dataset, Geo-Diverse Commonsense Multilingual Language Models Analysis (GeoMLAMA), for probing the diversity of the relational knowledge in multilingual PLMs.