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SemEval-2021 Task 1: Lexical Complexity Prediction

arXiv 2106.00473 ↗First posted 1 Jun 2021cs.CLcited by 19

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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.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 11 of its 83 references that are in the map. First posted on arXiv 1 Jun 2021.

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11 of 83 references.

References · 11

  1. 2020We Need to Talk About Random Splitscited by 70
  2. 2018A Report on the Complex Word Identification Shared Task 2018cited by 30
  3. 2018A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplificationcited by 24
  4. 2020CompLex: A New Corpus for Lexical Complexity Prediction from Likert Scale Datacited by 24
  5. 2017Complex Word Identification: Challenges in Data Annotation and System Performancecited by 8
  6. 2018UnibucKernel: A kernel-based learning method for complex word identificationcited by 5
  7. 2021UPB at SemEval-2021 Task 1: Combining Deep Learning and Hand-Crafted Features for Lexical Complexity Predictioncited by 5
  8. 2021IITK@LCP at SemEval 2021 Task 1: Classification for Lexical Complexity Regression Taskcited by 3
  9. 2021LCP-RIT at SemEval-2021 Task 1: Exploring Linguistic Features for Lexical Complexity Predictioncited by 3
  10. 2021BigGreen at SemEval-2021 Task 1: Lexical Complexity Prediction with Assembly Modelscited by 2
  11. 2021LAST at SemEval-2021 Task 1: Improving Multi-Word Complexity Prediction Using Bigram Association Measurescited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 5; 2024: 4; 2025: 2; 2026: 5.

19 papers.

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  1. 2023Findings of the TSAR-2022 Shared Task on Multilingual Lexical Simplificationcited by 20
  2. 2022Lexical Simplification Benchmarks for English, Portuguese, and Spanishcited by 18
  3. 2022One Size Does Not Fit All: The Case for Personalised Word Complexity Modelscited by 10
  4. 2023Teaching the Pre-trained Model to Generate Simple Texts for Text Simplificationcited by 10
  5. 2025Opportunities and Challenges of LLMs in Education: An NLP Perspectivecited by 6
  6. 2023Japanese Lexical Complexity for Non-Native Readers: A New Datasetcited by 5
  7. 2023Deep Learning Approaches to Lexical Simplification: A Surveycited by 4
  8. 2022Domain Adaptation in Multilingual and Multi-Domain Monolingual Settings for Complex Word Identificationcited by 3
  9. 2023Lexical Complexity Prediction: An Overviewcited by 3
  10. 2024Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain Setupscited by 3
  11. 2024Estimating Lexical Complexity from Document-Level Distributionscited by 2
  12. 2024Lexical Complexity Prediction and Lexical Simplification for Catalan and Spanish: Resource Creation, Quality Assessment, and Ethical Considerationscited by 1
7 more
  1. 2026RALS: Resources and Baselines for Romanian Automatic Lexical Simplificationcited by 1
  2. 2024MultiLS: A Multi-task Lexical Simplification Frameworkcited by 0
  3. 2025CEFR-Annotated WordNet: LLM-Based Proficiency-Guided Semantic Database for Language Learningcited by 0
  4. 2026Improving Lexical Difficulty Prediction with Context-Aligned Contrastive Learning and Ridge Ensemblingcited by 0
  5. 2026What makes a word hard to learn? Modeling L1 influence on English vocabulary difficultycited by 0
  6. 2026Sakura at BEA 2026 Shared Task 1: What Makes Vocabulary Difficult?cited by 0
  7. 2026UOL@IDEM at BEA 2026 Shared Task 1: Neural Fusion and Feature-Rich Modeling for L1-Aware Vocabulary Difficulty Predictioncited by 0