@inproceedings{pinney-etal-2024-incorporating,
    title = "Incorporating Word-level Phonemic Decoding into Readability Assessment",
    author = "Pinney, Christine  and
      Kennington, Casey  and
      Pera, Maria Soledad  and
      Landau Wright, Katherine  and
      Fails, Jerry Alan",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.788",
    pages = "8998--9009",
    abstract = "Current approaches in automatic readability assessment have found success with the use of large language models and transformer architectures. These techniques lead to accuracy improvement, but they do not offer the interpretability that is uniquely required by the audience most often employing readability assessment tools: teachers and educators. Recent work that employs more traditional machine learning methods has highlighted the linguistic importance of considering semantic and syntactic characteristics of text in readability assessment by utilizing handcrafted feature sets. Research in Education suggests that, in addition to semantics and syntax, phonetic and orthographic instruction are necessary for children to progress through the stages of reading and spelling development; children must first learn to decode the letters and symbols on a page to recognize words and phonemes and their connection to speech sounds. Here, we incorporate this word-level phonemic decoding process into readability assessment by crafting a phonetically-based feature set for grade-level classification for English. Our resulting feature set shows comparable performance to much larger, semantically- and syntactically-based feature sets, supporting the linguistic value of orthographic and phonetic considerations in readability assessment.",
}

Current approaches in automatic readability assessment have found success with the use of large language models and transformer architectures. These techniques lead to accuracy improvement, but they do not offer the interpretability that is uniquely required by the audience most often employing readability assessment tools: teachers and educators. Recent work that employs more traditional machine learning methods has highlighted the linguistic importance of considering semantic and syntactic characteristics of text in readability assessment by utilizing handcrafted feature sets. Research in Education suggests that, in addition to semantics and syntax, phonetic and orthographic instruction are necessary for children to progress through the stages of reading and spelling development; children must first learn to decode the letters and symbols on a page to recognize words and phonemes and their connection to speech sounds. Here, we incorporate this word-level phonemic decoding process into readability assessment by crafting a phonetically-based feature set for grade-level classification for English. Our resulting feature set shows comparable performance to much larger, semantically- and syntactically-based feature sets, supporting the linguistic value of orthographic and phonetic considerations in readability assessment.
Pinney, Christine Kennington, Casey Pera, Maria Soledad L au Wright, Katherine Fails, Jerry Alan
Incorporating Word-level Phonemic Decoding into Readability Assessment