@inproceedings{wu-2026-delayed,
    title = "Delayed Wh-Question Development in Children with Hearing Loss: Evidence for Morphosyntactic Vulnerability from Corpus-Based {NLP} and {LLM} Analyses",
    author = "Wu, Tong",
    editor = {Danilova, Vera  and
      Kurfal{\i}, Murathan  and
      S{\"o}derfeldt, Ylva  and
      Reed, Julia  and
      Burchell, Andrew},
    booktitle = "Proceedings of the 1st Workshop on Linguistic Analysis for Health ({H}ea{L}ing 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.healing-1.26/",
    pages = "291--304",
    ISBN = "979-8-89176-367-8",
    abstract = "This study provides corpus-based evidence that English-speaking children with hearing loss (CHL) show both quantitative and qualitative delays in wh-question development compared to typically developing (TD) peers. Using Natural Language Processing (NLP)/Large Language Model (LLM) based methods and two clinical subcorpora from CHILDES, we analyzed child utterances across several syntactic dimensions: frequency, lexical diversity, structural completeness, clausal embedding, wh-fronting, and utterance length. CHL produced significantly fewer wh-questions, used a narrower range of wh-types, showed lower rates of embedding, and more structural incompleteness. These differences were most evident in syntactically complex forms, such as embedded and canonical fronted wh-questions. The results support input-sensitive and usage-based accounts of syntactic development and highlight the need for enriched linguistic input in supporting CHL{'}s grammatical growth. Importantly, these group differences persisted when controlling for overalllanguage development as indexed by mean length of utterance (MLU) in words, indicatingthat CHL{'}s difficulties with wh-questions are not reducible to generalgrammatical delay.Methodologically, the study combines dependency-parsing-based analyses with exploratory LLM evaluation to assess the feasibility and limits of automated approaches to spontaneous child language. NLP-based analyses were more stable for formally defined syntactic features, while GPT-based analysis showed mixed performance, performing better on global structural judgments than on fine-grained syntactic diagnostics."
}