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 morphologically-aware
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.
64adfed880f308a395f3823e
Ester Hlavnova
64adfed880f308a395f3823f
Sebastian Ruder
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 morphologically-aware
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.
M2C, a morphologically-aware framework, tests and highlights generalization failures of NLP models to specific typological characteristics across 12 diverse languages.
morphologically-aware behavioral testing typological differences temporal expressions compounding possessives