LEXICAL CREATIVITY IN ENGLISH LANGUAGE CHICK LIT AND MACHINE TRANSLATION
DOI:
https://doi.org/10.31861/gph2026.858-859.12-25Keywords:
linguistic creativity, formation of neologisms, chick lit, English-Ukrainian machine translation, algorithmAbstract
The article examines lexical creativity in English language chick lit, focusing on neologisms formed through deliberate deviations from conventional word-formation rules. Such innovations, while stylistically and pragmatically significant within the genre, pose considerable challenges for translation, especially between typologically distant languages such as English and Ukrainian. In the context of rapid advancements in machine translation, it becomes increasingly important to evaluate the capacity of automated systems to process linguistically creative input in literary texts. This study investigates the performance of a widely used generic machine translation system, DeepL, in rendering creative neologisms into Ukrainian. The analysis is based on a corpus of neologisms extracted from contemporary chick lit novels, representing a range of morphological processes, including derivation, compounding, clipping, and conversion. Particular attention is paid to the interaction between formal innovation and semantic transparency, as well as to the extent to which machine translation can preserve both. The findings indicate that DeepL encounters substantial difficulties in translating creative neologisms, with error rates exceeding 80%. The most common issues include morphological mismatches, semantic loss, misinterpretation of constituent elements, and the production of non-viable or unnatural target-language forms. These errors suggest a systematic limitation in the system’s ability to process deviations from entrenched linguistic patterns. Overall, the results demonstrate that the very features that make neologisms creative – novelty, unpredictability, and context sensitivity – are precisely those that fall outside the distributional and probabilistic capacities of current machine translation algorithms. The study underscores the need for enhanced translation models and informed post-editing strategies to better capture stylistic nuance and linguistic creativity in literary translation.
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