  This paper describes our contribution to SemEval 2021 Task 1: Lexical
Complexity Prediction. In our approach, we leverage the ELECTRA model and
attempt to mirror the data annotation scheme. Although the task is a regression
task, we show that we can treat it as an aggregation of several classification
and regression models. This somewhat counter-intuitive approach achieved an MAE
score of 0.0654 for Sub-Task 1 and MAE of 0.0811 on Sub-Task 2. Additionally,
we used the concept of weak supervision signals from Gloss-BERT in our work,
and it significantly improved the MAE score in Sub-Task 1.
