@inproceedings{kong-macken-2025-peter,
    title = "Can Peter Pan Survive {MT}? A Stylometric Study of {LLM}s, {NMT}s, and {HT}s in Children{'}s Literature Translation",
    author = "Kong, Delu  and
      Macken, Lieve",
    editor = "Vanroy, Bram  and
      Lefer, Marie-Aude  and
      Macken, Lieve  and
      Ruffo, Paola  and
      Arenas, Ana Guerberof  and
      Hansen, Damien",
    booktitle = "Proceedings of the Second Workshop on Creative-text Translation and Technology (CTT)",
    month = jun,
    year = "2025",
    address = "Geneva, Switzerland",
    publisher = "European Association for Machine Translation",
    url = "https://preview.aclanthology.org/mtsummit-25-ingestion/2025.ctt-1.5/",
    pages = "52--70",
    ISBN = "978-2-9701897-6-3",
    abstract = "This study focuses on evaluating the performance of machine translations (MTs) compared to human translations (HTs) in children{'}s literature translation (CLT) from a stylometric perspective. The research constructs a extitPeter Pan corpus, comprising 21 translations: 7 human translations (HTs), 7 large language model translations (LLMs), and 7 neural machine translation outputs (NMTs). The analysis employs a generic feature set (including lexical, syntactic, readability, and n-gram features) and a creative text translation (CTT-specific) feature set, which captures repetition, rhyme, translatability, and miscellaneous levels, yielding 447 linguistic features in total. Using classification and clustering techniques in machine learning, we conduct a stylometric analysis of these translations. Results reveal that in generic features, HTs and MTs exhibit significant differences in conjunction word distributions and the ratio of 1-word-gram-一样, while NMTs and LLMs show significant variation in descriptive words usage and adverb ratios. Regarding CTT-specific features, LLMs outperform NMTs in distribution, aligning more closely with HTs in stylistic characteristics, demonstrating the potential of LLMs in CLT."
}