Neil Shirude


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IITK@LCP at SemEval-2021 Task 1: Classification for Lexical Complexity Regression Task
Neil Shirude | Sagnik Mukherjee | Tushar Shandhilya | Ananta Mukherjee | Ashutosh Modi
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

This paper describes our contribution to SemEval 2021 Task 1 (Shardlow et al., 2021): 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.