@inproceedings{shirude-etal-2021-iitk,
title = "{IITK}@{LCP} at {S}em{E}val-2021 Task 1: Classification for Lexical Complexity Regression Task",
author = "Shirude, Neil and
Mukherjee, Sagnik and
Shandhilya, Tushar and
Mukherjee, Ananta and
Modi, Ashutosh",
editor = "Palmer, Alexis and
Schneider, Nathan and
Schluter, Natalie and
Emerson, Guy and
Herbelot, Aurelie and
Zhu, Xiaodan",
booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.semeval-1.66",
doi = "10.18653/v1/2021.semeval-1.66",
pages = "541--547",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T IITK@LCP at SemEval-2021 Task 1: Classification for Lexical Complexity Regression Task
%A Shirude, Neil
%A Mukherjee, Sagnik
%A Shandhilya, Tushar
%A Mukherjee, Ananta
%A Modi, Ashutosh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Schluter, Natalie
%Y Emerson, Guy
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%S Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F shirude-etal-2021-iitk
%X 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.
%R 10.18653/v1/2021.semeval-1.66
%U https://aclanthology.org/2021.semeval-1.66
%U https://doi.org/10.18653/v1/2021.semeval-1.66
%P 541-547
Markdown (Informal)
[IITK@LCP at SemEval-2021 Task 1: Classification for Lexical Complexity Regression Task](https://aclanthology.org/2021.semeval-1.66) (Shirude et al., SemEval 2021)
ACL