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.
@article{arxiv.2104.01046,
title = {IITK@LCP at SemEval 2021 Task 1: Classification for Lexical Complexity Regression Task},
author = {Neil Rajiv Shirude and Sagnik Mukherjee and Tushar Shandhilya and Ananta Mukherjee and Ashutosh Modi},
journal= {arXiv preprint arXiv:2104.01046},
year = {2021}
}
Comments
Accepted at SemEval 2021 Task 1, 7 Pages (5 Pages main content+ 2 pages for reference)