How Does Data Corruption Affect Natural Language Understanding Models? A Study on GLUE datasets

• URL: http://arxiv.org/abs/2201.04467v1
• Date: Wed, 12 Jan 2022 13:35:53 GMT
• Title: How Does Data Corruption Affect Natural Language Understanding Models? A Study on GLUE datasets
• Authors: Aarne Talman, Marianna Apidianaki, Stergios Chatzikyriakidis, J\"org Tiedemann
• Abstract summary: We show that performance remains high for most GLUE tasks when the models are fine-tuned or tested on corrupted data. Our proposed data transformations can be used as a diagnostic tool for assessing the extent to which a specific dataset constitutes a proper testbed for evaluating models' language understanding capabilities.
• Score: 4.645287693363387
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. The transformations involve removing instances of specific word classes and often lead to non-sensical sentences. Our results show that performance remains high for most GLUE tasks when the models are fine-tuned or tested on corrupted data, suggesting that the models leverage other cues for prediction even in non-sensical contexts. Our proposed data transformations can be used as a diagnostic tool for assessing the extent to which a specific dataset constitutes a proper testbed for evaluating models' language understanding capabilities.

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