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

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 classificati...

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Bibliographic Details
Published in:arXiv.org 2021-04
Main Authors: Neil Rajiv Shirude, Mukherjee, Sagnik, Shandhilya, Tushar, Mukherjee, Ananta, Modi, Ashutosh
Format: Article
Language:English
Subjects:
Annotations
Classification
Complexity
Gloss
Regression models
Online Access:Get full text
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Summary: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.
ISSN:2331-8422