@inproceedings{yang-etal-2025-chinese,
    title = "{C}hinese Automatic Readability Assessment Using Adaptive Pre-training and Linguistic Feature Fusion",
    author = "Yang, Xusheng  and
      Yang, Jincai  and
      Li, Xiao",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.coling-main.605/",
    pages = "9013--9024",
    abstract = "Chinese Automatic Readability Assessment (ARA) aims to classify the reading difficulty of Chinese texts. To address the issues of insufficient high-quality training data and underutilization of linguistic features in existing methods, we propose a method that combines adaptive pre-training with feature fusion based on an interactive attention mechanism. First, we enhance the model{'}s ability to capture different text difficulties through domain- and task-specific adaptive pre-training. Then, we propose an Adaptive Task-guided Corpus Filtering (ATCF) method, utilizing embeddings generated by the pre-trained model and applying nearest-neighbor search along with a sample balancing mechanism to ensure comprehensive learning across various difficulty levels. Finally, we propose an Interactive Attention-Driven Feature Fusion method that integrates linguistic and deep features, providing rich difficulty information to the model. Experiments on Chinese textbook dataset demonstrate that our method achieves state-of-the-art (SOTA) performance. Transfer learning experiments further indicate that our approach generalizes well to extracurricular reading and Chinese as a Foreign Language (CFL) ARA tasks."
}