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    <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.</abstract>
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%0 Conference Proceedings
%T Can Peter Pan Survive MT? A Stylometric Study of LLMs, NMTs, and HTs in Children’s Literature Translation
%A Kong, Delu
%A Macken, Lieve
%Y Vanroy, Bram
%Y Lefer, Marie-Aude
%Y Macken, Lieve
%Y Ruffo, Paola
%Y Arenas, Ana Guerberof
%Y Hansen, Damien
%S Proceedings of the Second Workshop on Creative-text Translation and Technology (CTT)
%D 2025
%8 June
%I European Association for Machine Translation
%C Geneva, Switzerland
%@ 978-2-9701897-6-3
%F kong-macken-2025-peter
%X 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.
%U https://aclanthology.org/2025.ctt-1.5/
%P 52-70

Markdown (Informal)

[Can Peter Pan Survive MT? A Stylometric Study of LLMs, NMTs, and HTs in Children’s Literature Translation](https://aclanthology.org/2025.ctt-1.5/) (Kong & Macken, CTT 2025)

• Can Peter Pan Survive MT? A Stylometric Study of LLMs, NMTs, and HTs in Children’s Literature Translation (Kong & Macken, CTT 2025)

ACL

• Delu Kong and Lieve Macken. 2025. Can Peter Pan Survive MT? A Stylometric Study of LLMs, NMTs, and HTs in Children’s Literature Translation. In Proceedings of the Second Workshop on Creative-text Translation and Technology (CTT), pages 52–70, Geneva, Switzerland. European Association for Machine Translation.