  This paper describes our system for Task 4 of SemEval-2021: Reading
Comprehension of Abstract Meaning (ReCAM). We participated in all subtasks
where the main goal was to predict an abstract word missing from a statement.
We fine-tuned the pre-trained masked language models namely BERT and ALBERT and
used an Ensemble of these as our submitted system on Subtask 1
(ReCAM-Imperceptibility) and Subtask 2 (ReCAM-Nonspecificity). For Subtask 3
(ReCAM-Intersection), we submitted the ALBERT model as it gives the best
results. We tried multiple approaches and found that Masked Language
Modeling(MLM) based approach works the best.
