mlsanityData to 7 Oct 2026

Japanese Lexical Complexity for Non-Native Readers: A New Dataset

arXiv 2306.17399 ↗First posted 30 Jun 2023cs.CLcited by 5

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Abstract (arXiv)

Lexical complexity prediction (LCP) is the task of predicting the complexity of words in a text on a continuous scale. It plays a vital role in simplifying or annotating complex words to assist readers. To study lexical complexity in Japanese, we construct the first Japanese LCP dataset. Our dataset provides separate complexity scores for Chinese/Korean annotators and others to address the readers' L1-specific needs. In the baseline experiment, we demonstrate the effectiveness of a BERT-based system for Japanese LCP.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 8 of its 30 references that are in the map. First posted on arXiv 30 Jun 2023.

Builds on

8 of 30 references.

References · 8

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2018A Report on the Complex Word Identification Shared Task 2018cited by 30
  3. 2020CompLex: A New Corpus for Lexical Complexity Prediction from Likert Scale Datacited by 24
  4. 2021SemEval-2021 Task 1: Lexical Complexity Predictioncited by 19
  5. 2022Lexical Simplification Benchmarks for English, Portuguese, and Spanishcited by 18
  6. 2021Predicting Lexical Complexity in English Texts: The Complex 2.0 Datasetcited by 15
  7. 2022One Size Does Not Fit All: The Case for Personalised Word Complexity Modelscited by 10
  8. 2020Detecting Multiword Expression Type Helps Lexical Complexity Assessmentcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 3.

5 papers.