Cross-Lingual Approaches for Text Difficulty Classification in Non-English Languages - Dai, Huili (Informatica, 2025)
Text Difficulty Classification (TDC) significantly contributes to educational technology, language learning, and information access by automatically predicting the difficulty of texts. While English has been favored with extensive resources and mature readability tools, non-English languages suffer from sparse annotated corpora, linguistic diversity, and insufficient computational resources. Cross-lingual methods, including shared knowledge transfer from high-resource to low-resource languages, have become increasingly popular in recent years. It covers the latest advances in cross-lingual TDC in pipelines of machine translation-based, multi-lingual pre-trained transformers (e.g., mBERT, XLM-R), adversarial and meta-learning-based approaches, and knowledge distillation techniques. It also summarizes available datasets and benchmarks, critiques current practices, and highlights morphological complexity, translation artifacts, and domain mismatch. It identifies significant trends and future promise by critically examining state-of-the-art methods for multimodal TDC, hybrid architectures, and active learning in low-resource languages. The results highlight the significance of non-discriminatory, resource-lean systems for measuring fair-minded text difficulty in many languages.