Empowering Cross-lingual Behavioral Testing of NLP Models with Typological Features

A challenge towards developing NLP systems for the world's languages is understanding how they generalize to typological differences relevant for real-world applications. To this end, we propose M2C, a morphologicallyaware framework for behavioral testing of NLP models. We use M2C to generate tests that probe models' behavior in light of specific linguistic features in 12 typologically diverse languages. We evaluate state-of-the-art language models on the generated tests. While models excel at most tests in English, we highlight generalization failures to specific typological characteristics such as temporal expressions in Swahili and compounding possessives in Finish. Our findings motivate the development of models that address these blind spots. 1 1 We make all code publicly available at https://github. com/google-research/multi-morph-checklist . 2 For instance, while tone is present in around 80% of African languages (Adebara and Abdul-Mageed, 2022), few Indo-European languages can be considered tonal. 0.0 25.0 50.0 75.0 100.0 English Slovak Chinese Swahili Average (across 12 languages)