This is the codebase for VerAs: Verify then Assess STEM Lab Reports. We propose an end-to-end neural architecture (VerAs) that has separate verifier and assessment modules for a formative assessment of longer forms of student writing with rubrics. Below, you can find the figure for VerAs.

We experiment with two different datasets: college physics and middle school essays. You can find the commands for both below.

1. College Physics: python main.py --top_k <topk> --dataset_name college_physics --verifier_model <verifier_model> --grader_model <grader_model> --loss_function <loss> --oll_loss_alpha <alpha>
2. Middle School Essays: python main.py --top_k <topk> --dataset_name middle_school --verifier_model <verifier_model> --grader_model <grader_model> --loss_function <loss> --oll_loss_alpha <alpha>

where <topk> is a parameter that represents the number of relevant sentences retrieved by the verifier, <verifier_model> is the language model for the verifier (currently, we support bert and sbert), <grader_model> is the language model for the grader (currently, we support longt5, bert, and electra), <loss> is the loss function to use which can be cross_entropy or ordinal log loss (oll), and <alpha> is the parameter for oll.

python evalaute.py <grader_file_path> <verifier_file_path> <dataset_name>

where the <grader_file_path> is a path to the pretrained grader model, <verifier_file_path> is a path to the pretrained verifier model, and <dataset_name> is the name of the dataset.