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During the past several years, a surge of multi-lingual Pre-trained LanguageModels (PLMs) has been proposed to achieve state-of-the-art performance in manycross-lingual downstream tasks. However, the understanding of why multi-lingualPLMs perform well is still an open domain. For example, it is unclear whethermulti-Lingual PLMs reveal consistent token attributions in different languages.To address this, in this paper, we propose a Cross-lingual Consistency of TokenAttributions (CCTA) evaluation framework. Extensive experiments in threedownstream tasks demonstrate that multi-lingual PLMs assign significantlydifferent attributions to multi-lingual synonyms. Moreover, we have thefollowing observations: 1) the Spanish achieves the most consistent tokenattributions in different languages when it is used for training PLMs; 2) theconsistency of token attributions strongly correlates with performance indownstream tasks.

Do Multi-Lingual Pre-trained Language Models Reveal Consistent Token Attributions in Different Languages?