Papers › IITK@LCP at SemEval 2021 Task 1: Classification for Lexical Complexity Regression Task

IITK@LCP at SemEval 2021 Task 1: Classification for Lexical Complexity Regression Task

2 Apr 2021arXiv:2104.01046archive 2025-07-28

Neil Rajiv Shirude, Sagnik Mukherjee, Tushar Shandhilya, Ananta Mukherjee, Ashutosh Modi

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.

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General Classification Lexical Complexity Prediction Task 2 regression

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Methods

Adam Attention Attention Dropout Dense Connections Dropout ELECTRA Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Softmax Weight Decay WordPiece

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