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

arXiv: https://arxiv.org/abs/2104.01046 · HTML: https://mlsanity.com/paper/2104.01046/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 2 Apr 2021.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [All-but-the-Top: Simple and Effective Postprocessing for Word Representations](https://mlsanity.com/paper/1702.01417/) (2017)
- [GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge](https://mlsanity.com/paper/1908.07245/) (2019)
- [FRAGE: Frequency-Agnostic Word Representation](https://mlsanity.com/paper/1809.06858/) (2018)
- [A Report on the Complex Word Identification Shared Task 2018](https://mlsanity.com/paper/1804.09132/) (2018)
- [CompLex: A New Corpus for Lexical Complexity Prediction from Likert Scale Data](https://mlsanity.com/paper/2003.07008/) (2020)

## Built on by (most cited first, 3 of 3)

- [SemEval-2021 Task 1: Lexical Complexity Prediction](https://mlsanity.com/paper/2106.00473/) (2021)
- [Lexical Complexity Prediction: An Overview](https://mlsanity.com/paper/2303.04851/) (2023)
- [IITK at SemEval-2024 Task 1: Contrastive Learning and Autoencoders for Semantic Textual Relatedness in Multilingual Texts](https://mlsanity.com/paper/2404.04513/) (2024)

## Abstract (arXiv)

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.