# SemEval-2021 Task 1: Lexical Complexity Prediction

arXiv: https://arxiv.org/abs/2106.00473 · HTML: https://mlsanity.com/paper/2106.00473/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 83 references that are in the map.
- First posted on arXiv 1 Jun 2021.

## Builds on (most cited first, 11 of 11)

- [We Need to Talk About Random Splits](https://mlsanity.com/paper/2005.00636/) (2020)
- [A Report on the Complex Word Identification Shared Task 2018](https://mlsanity.com/paper/1804.09132/) (2018)
- [A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification](https://mlsanity.com/paper/1810.05754/) (2018)
- [CompLex: A New Corpus for Lexical Complexity Prediction from Likert Scale Data](https://mlsanity.com/paper/2003.07008/) (2020)
- [Complex Word Identification: Challenges in Data Annotation and System Performance](https://mlsanity.com/paper/1710.04989/) (2017)
- [UnibucKernel: A kernel-based learning method for complex word identification](https://mlsanity.com/paper/1803.07602/) (2018)
- [UPB at SemEval-2021 Task 1: Combining Deep Learning and Hand-Crafted Features for Lexical Complexity Prediction](https://mlsanity.com/paper/2104.06983/) (2021)
- [IITK@LCP at SemEval 2021 Task 1: Classification for Lexical Complexity Regression Task](https://mlsanity.com/paper/2104.01046/) (2021)
- [LCP-RIT at SemEval-2021 Task 1: Exploring Linguistic Features for Lexical Complexity Prediction](https://mlsanity.com/paper/2105.08780/) (2021)
- [BigGreen at SemEval-2021 Task 1: Lexical Complexity Prediction with Assembly Models](https://mlsanity.com/paper/2104.09040/) (2021)
- [LAST at SemEval-2021 Task 1: Improving Multi-Word Complexity Prediction Using Bigram Association Measures](https://mlsanity.com/paper/2105.09653/) (2021)

## Built on by (most cited first, 19 of 19)

- [Findings of the TSAR-2022 Shared Task on Multilingual Lexical Simplification](https://mlsanity.com/paper/2302.02888/) (2023)
- [Lexical Simplification Benchmarks for English, Portuguese, and Spanish](https://mlsanity.com/paper/2209.05301/) (2022)
- [One Size Does Not Fit All: The Case for Personalised Word Complexity Models](https://mlsanity.com/paper/2205.02564/) (2022)
- [Teaching the Pre-trained Model to Generate Simple Texts for Text Simplification](https://mlsanity.com/paper/2305.12463/) (2023)
- [Opportunities and Challenges of LLMs in Education: An NLP Perspective](https://mlsanity.com/paper/2507.22753/) (2025)
- [Japanese Lexical Complexity for Non-Native Readers: A New Dataset](https://mlsanity.com/paper/2306.17399/) (2023)
- [Deep Learning Approaches to Lexical Simplification: A Survey](https://mlsanity.com/paper/2305.12000/) (2023)
- [Domain Adaptation in Multilingual and Multi-Domain Monolingual Settings for Complex Word Identification](https://mlsanity.com/paper/2205.07283/) (2022)
- [Lexical Complexity Prediction: An Overview](https://mlsanity.com/paper/2303.04851/) (2023)
- [Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain Setups](https://mlsanity.com/paper/2411.01706/) (2024)
- [Estimating Lexical Complexity from Document-Level Distributions](https://mlsanity.com/paper/2404.01196/) (2024)
- [Lexical Complexity Prediction and Lexical Simplification for Catalan and Spanish: Resource Creation, Quality Assessment, and Ethical Considerations](https://mlsanity.com/paper/2404.07814/) (2024)
- [RALS: Resources and Baselines for Romanian Automatic Lexical Simplification](https://mlsanity.com/paper/2607.20078/) (2026)
- [MultiLS: A Multi-task Lexical Simplification Framework](https://mlsanity.com/paper/2402.14972/) (2024)
- [CEFR-Annotated WordNet: LLM-Based Proficiency-Guided Semantic Database for Language Learning](https://mlsanity.com/paper/2510.18466/) (2025)
- [Improving Lexical Difficulty Prediction with Context-Aligned Contrastive Learning and Ridge Ensembling](https://mlsanity.com/paper/2605.08950/) (2026)
- [What makes a word hard to learn? Modeling L1 influence on English vocabulary difficulty](https://mlsanity.com/paper/2605.12281/) (2026)
- [Sakura at BEA 2026 Shared Task 1: What Makes Vocabulary Difficult?](https://mlsanity.com/paper/2605.14257/) (2026)
- [UOL@IDEM at BEA 2026 Shared Task 1: Neural Fusion and Feature-Rich Modeling for L1-Aware Vocabulary Difficulty Prediction](https://mlsanity.com/paper/2606.24501/) (2026)

## Abstract (arXiv)

This paper presents the results and main findings of SemEval-2021 Task 1 - Lexical Complexity Prediction. We provided participants with an augmented version of the CompLex Corpus (Shardlow et al 2020). CompLex is an English multi-domain corpus in which words and multi-word expressions (MWEs) were annotated with respect to their complexity using a five point Likert scale. SemEval-2021 Task 1 featured two Sub-tasks: Sub-task 1 focused on single words and Sub-task 2 focused on MWEs. The competition attracted 198 teams in total, of which 54 teams submitted official runs on the test data to Sub-task 1 and 37 to Sub-task 2.