How Does Data Corruption Affect Natural Language Understanding Models? A Study on GLUE datasets
Aarne Talman, Marianna Apidianaki, Stergios Chatzikyriakidis, Jörg Tiedemann · 2022
A central question in natural language understanding (NLU) research is whether high performance demonstrates the models' strong reasoning capabilities.We present an extensive series of controlled experiments where pre-trained language models are exposed to data that have undergone specific corruption transformations.These involve removing instances of specific word classes and often lead to non-sensical sentences.Our results show that performance remains high on most GLUE tasks when the models are fine-tuned or tested on corrupted data, suggesting that they leverage other cues for prediction even in non-sensical contexts.Our proposed data transformations can be used to assess the extent to which a specific dataset constitutes a proper testbed for evaluating models' language understanding capabilities.