Songs Across Borders: Singable and Controllable Neural Lyric Translation (2023.acl-long)
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| Challenge: | Obtaining singable lyric translations can facilitate the globalization of the music publishing industry . |
| Approach: | They formalize lyric translation into a constrained translation problem and instantiate them to an English-Chinese system. |
| Outcome: | The proposed model achieves 99.85%, 99.00%, and 95.52% on length accuracy, rhyme accuracy, and word boundary recall. |
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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. |
Automatic Song Translation for Tonal Languages (2022.findings-acl)
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| Challenge: | Existing automatic song translation systems for tonal languages do not match the number of notes and beat the original rhythm of the song. |
| Approach: | They propose three criteria for effective AST: preserving meaning, singability and intelligibility. |
| Outcome: | The proposed system balances semantics and singability with human evaluations. |
Translate the Beauty in Songs: Jointly Learning to Align Melody and Translate Lyrics (2023.findings-emnlp)
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| Challenge: | Song translation requires both translation of lyrics and alignment of music notes . human translators of songs need to have a mastery of cultural traditions and the poetic usage of both source and target languages . |
| Approach: | They propose a model that can model lyric translation and lyrics-melody alignment . they use an encoder-decoder framework that can translate lyrics and determine number of aligned notes . |
| Outcome: | The proposed framework can translate lyrics and determine the number of aligned notes at each decoding step. |
Towards Singable Lyrics Translation Using Large Language Models (2026.eacl-srw)
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| Challenge: | Existing studies on lyrics translation have relied on fine-tuning open-source language models. |
| Approach: | They examine a multilingual lyrics translation dataset and apply prompting methods to large language models to evaluate singability. |
| Outcome: | The proposed methods improve singability and naturalness, compared to naive translation, the authors show . human evaluations using songs created from translated lyrics show that complex prompting strategies improve singable naturalness . |
Sing it, Narrate it: Quality Musical Lyrics Translation (2024.findings-emnlp)
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| Challenge: | Existing song translation approaches prioritize singability constraints at the expense of translation quality, which is crucial for musicals. |
| Approach: | They propose to automatically translate musical lyrics from English to Chinese to ensure high translation quality while adhering to singability requirements such as length and rhyme. |
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Bridging the Gap between Training and Inference for Neural Machine Translation (P19-1)
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| Challenge: | Neural Machine Translation generates target words sequentially while at inference it has to generate the entire sequence from scratch. |
| Approach: | They propose to use ground truth and inference to generate target words sequentially while at inference it has to generate the entire sequence from scratch. |
| Outcome: | Experiments on Chinese->English and WMT’14 English->German translation tasks show that the proposed model can achieve significant improvements on multiple datasets. |
On the Importance of Word Boundaries in Character-level Neural Machine Translation (D19-56)
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| Challenge: | Neural Machine Translation models typically use a fixed-size lexical vocabulary . subword segmentation methods rely on statistical heuristics that lack any linguistic notion . |
| Approach: | They propose a hierarchical decoding architecture for character-level NMT using subwords . they propose fewer parameters and a more efficient approach to perform translation at the level of words . |
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Beyond Noise: Mitigating the Impact of Fine-grained Semantic Divergences on Neural Machine Translation (2021.acl-long)
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| Challenge: | Prior work treats all types of mismatches between source and target as noise . Consequently, it remains unclear how noisy parallel training samples impact NMT training. |
| Approach: | They propose a divergent-aware NMT framework that uses factors to help NMT recover from the degradation caused by naturally occurring divergences. |
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Integrating Vectorized Lexical Constraints for Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing studies focus on integrating discrete lexical constraints into neural machine translation models. |
| Approach: | They propose to integrate constraints into NMT models by integrating them into keys and values . they show that their method outperforms representative baselines on four language pairs . |
| Outcome: | The proposed method outperforms baselines on four language pairs, showing superiority . |
A Melody-Conditioned Lyrics Language Model (N18-1)
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Kento Watanabe, Yuichiroh Matsubayashi, Satoru Fukayama, Masataka Goto, Kentaro Inui, Tomoyasu Nakano
| Challenge: | Existing models for lyrics generation are insufficient to capture relationship between lyrics and melody. |
| Approach: | They propose a data-driven language model that generates entire lyrics for a given melody. |
| Outcome: | The proposed model generates fluent lyrics while maintaining compatibility between lyrics and melodies. |