K-pop Lyric Translation: Dataset, Analysis, and Neural-Modelling (2024.lrec-main)
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| Challenge: | lyric translation studies have focused on Western genres and languages, with no previous study centering on K-pop despite its popularity. |
| Approach: | They propose a singable lyric translation dataset that aligns Korean and English lyrics line-by-line and section-by section. |
| Outcome: | The proposed dataset reveals unique characteristics of K-pop lyric translation, distinguishing it from other extensively studied genres, and constructs a neural lyrical translation model. |
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| Challenge: | Obtaining singable lyric translations can facilitate the globalization of the music publishing industry . |
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Kento Watanabe, Yuichiroh Matsubayashi, Satoru Fukayama, Masataka Goto, Kentaro Inui, Tomoyasu Nakano
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| Challenge: | Existing studies on lyrics translation have relied on fine-tuning open-source language models. |
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Yufei Tian, Anjali Narayan-Chen, Shereen Oraby, Alessandra Cervone, Gunnar Sigurdsson, Chenyang Tao, Wenbo Zhao, Tagyoung Chung, Jing Huang, Nanyun Peng
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| Challenge: | Pre-trained language models specifically designed at the syllable level are not available. |
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| Challenge: | Existing methods of generating singable lyrics are based on a given melody, but there are two main challenges: generating the lyrics without knowing the melody and composing compatible melodies. |
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