  Research towards creating systems for automatic grading of student answers to
quiz and exam questions in educational settings has been ongoing since 1966.
Over the years, the problem was divided into many categories. Among them,
grading text answers were divided into short answer grading, and essay grading.
The goal of this work was to develop an ML-based short answer grading system. I
hence built a system which uses finetuning on Roberta Large Model pretrained on
STS benchmark dataset and have also created an interface to show the production
readiness of the system. I evaluated the performance of the system on the
Mohler extended dataset and SciEntsBank Dataset. The developed system achieved
a Pearsons Correlation of 0.82 and RMSE of 0.7 on the Mohler Dataset which
beats the SOTA performance on this dataset which is correlation of 0.805 and
RMSE of 0.793. Additionally, Pearsons Correlation of 0.79 and RMSE of 0.56 was
achieved on the SciEntsBank Dataset, which only reconfirms the robustness of
the system. A few observations during achieving these results included usage of
batch size of 1 produced better results than using batch size of 16 or 32 and
using huber loss as loss function performed well on this regression task. The
system was tried and tested on train and validation splits using various random
seeds and still has been tweaked to achieve a minimum of 0.76 of correlation
and a maximum 0.15 (out of 1) RMSE on any dataset.
