This study is a comparative analysis of translated-text error-rates in song lyrics by K-Pop group New Jeans. Seven non-official machine translations (MTs) of ten songs were analyzed against official human-translated lyrics. The ten songs were consisted of a total 235 segments and the seven MTs were categorized under neural-network types (DeepL, Papago, Google Translate) and generative-AI types (ChatGPT, Bard, ClovaX, MS Bing Translate). Analysis discovered three salient points. First, neural-network types presented significantly higher error rates than generative-AI types. DeepL (66%), Papago (64%), Google Translate (59%). The most common errors were semantic and grammatical. A common feature of the errors was the poor contextual understanding and consistency between consecutive segments. This suggests that neural network MTs may have limited application for translating K-pop lyrics, which are expressive text. Second, neural network MTs were twice as erroneous as generative AI translations, with the official human translation as the baseline. T his suggests that AI translation may be more useful for translating K -pop lyrics than neural network MT in terms of semantic accuracy an d structural form. Third, generative AI translation quality improved in general when additional parameters and descriptions were provided via the services’ chat functions.