NLU Dataset Diagnostics

This repository contains data and scripts to reproduce the results from our paper:

Aarne Talman, Marianna Apidianaki, Stergios Chatzikyriakidis, Jörg Tiedemann. 2022. How Does Data Corruption Affect Natural Language Understanding Models? A Study on GLUE datasets.

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.

Reproduce our results

Install the dependencies by running:

pip install -r requirements.txt

Run the experiments using the following command:

bash run_experiment.sh

run_experiment.sh starts a fine-tuning job for each configuration and only works in an environment where you have access to a lot of GPU instances managed with an orchestration system like SLURM. To run a single configuration, you can modify train.sh and tun it:

bash train.sh

The Python script run_corrupt_glue.py is a modified version of the run_glue.py script by Huggingface available in their Text classification examples.

Cite our paper

@misc{talman_et_al2022,
      title={How Does Data Corruption Affect Natural Language Understanding Models? A Study on GLUE datasets}, 
      author={Aarne Talman and Marianna Apidianaki and Stergios Chatzikyriakidis and J\"org Tiedemann},
      year={2022},
      eprint={2201.04467},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}