# A Report on the Complex Word Identification Shared Task 2018

arXiv: https://arxiv.org/abs/1804.09132 · HTML: https://mlsanity.com/paper/1804.09132/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2018.

## Built on by (most cited first, 30 of 30)

- [Findings of the TSAR-2022 Shared Task on Multilingual Lexical Simplification](https://mlsanity.com/paper/2302.02888/) (2023)
- [SemEval-2021 Task 1: Lexical Complexity Prediction](https://mlsanity.com/paper/2106.00473/) (2021)
- [Lexical Simplification Benchmarks for English, Portuguese, and Spanish](https://mlsanity.com/paper/2209.05301/) (2022)
- [Predicting Lexical Complexity in English Texts: The Complex 2.0 Dataset](https://mlsanity.com/paper/2102.08773/) (2021)
- [A Survey on Using Gaze Behaviour for Natural Language Processing](https://mlsanity.com/paper/2112.15471/) (2021)
- [One Size Does Not Fit All: The Case for Personalised Word Complexity Models](https://mlsanity.com/paper/2205.02564/) (2022)
- [Exploring Large Language Models to generate Easy to Read content](https://mlsanity.com/paper/2407.20046/) (2024)
- [Japanese Lexical Complexity for Non-Native Readers: A New Dataset](https://mlsanity.com/paper/2306.17399/) (2023)
- [An LLM-Enhanced Adversarial Editing System for Lexical Simplification](https://mlsanity.com/paper/2402.14704/) (2024)
- [Deep Learning Approaches to Lexical Simplification: A Survey](https://mlsanity.com/paper/2305.12000/) (2023)
- [Difficult for Whom? A Study of Japanese Lexical Complexity](https://mlsanity.com/paper/2410.18567/) (2024)
- [CoAM: Corpus of All-Type Multiword Expressions](https://mlsanity.com/paper/2412.18151/) (2024)
- [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)
- [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)
- [BigGreen at SemEval-2021 Task 1: Lexical Complexity Prediction with Assembly Models](https://mlsanity.com/paper/2104.09040/) (2021)
- [MultiAzterTest: a Multilingual Analyzer on Multiple Levels of Language for Readability Assessment](https://mlsanity.com/paper/2109.04870/) (2021)
- [Controllable Lexical Simplification for English](https://mlsanity.com/paper/2302.02900/) (2023)
- [Estimating Lexical Complexity from Document-Level Distributions](https://mlsanity.com/paper/2404.01196/) (2024)
- [Semi-Supervised Joint Estimation of Word and Document Readability](https://mlsanity.com/paper/2104.13103/) (2021)
- [SimpleBERT: A Pre-trained Model That Learns to Generate Simple Words](https://mlsanity.com/paper/2204.07779/) (2022)
- [Lexical Complexity Prediction and Lexical Simplification for Catalan and Spanish: Resource Creation, Quality Assessment, and Ethical Considerations](https://mlsanity.com/paper/2404.07814/) (2024)
- [Readability-guided Idiom-aware Sentence Simplification (RISS) for Chinese](https://mlsanity.com/paper/2406.02974/) (2024)
- [RALS: Resources and Baselines for Romanian Automatic Lexical Simplification](https://mlsanity.com/paper/2607.20078/) (2026)
- [OCHADAI-KYOTO at SemEval-2021 Task 1: Enhancing Model Generalization and Robustness for Lexical Complexity Prediction](https://mlsanity.com/paper/2105.05535/) (2021)
- [MultiLS: A Multi-task Lexical Simplification Framework](https://mlsanity.com/paper/2402.14972/) (2024)
- [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)

## Abstract (arXiv)

We report the findings of the second Complex Word Identification (CWI) shared task organized as part of the BEA workshop co-located with NAACL-HLT'2018. The second CWI shared task featured multilingual and multi-genre datasets divided into four tracks: English monolingual, German monolingual, Spanish monolingual, and a multilingual track with a French test set, and two tasks: binary classification and probabilistic classification. A total of 12 teams submitted their results in different task/track combinations and 11 of them wrote system description papers that are referred to in this report and appear in the BEA workshop proceedings.