Automatic Song Translation for Tonal Languages (2022.findings-acl)

Copied to clipboard

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.

Similar Papers

Songs Across Borders: Singable and Controllable Neural Lyric Translation (2023.acl-long)

Copied to clipboard

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.
Translate the Beauty in Songs: Jointly Learning to Align Melody and Translate Lyrics (2023.findings-emnlp)

Copied to clipboard

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.
K-pop Lyric Translation: Dataset, Analysis, and Neural-Modelling (2024.lrec-main)

Copied to clipboard

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.
Sing it, Narrate it: Quality Musical Lyrics Translation (2024.findings-emnlp)

Copied to clipboard

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.
Outcome: The proposed method improves both singability and translation quality over baseline methods and validates its effectiveness.
SongComposer: A Large Language Model for Lyric and Melody Generation in Song Composition (2025.acl-long)

Copied to clipboard

Challenge: Creating lyrics and melodies in symbolic format requires expert knowledge of melody and an advanced understanding of lyrics.
Approach: They introduce SongComposer, a music-specialized large language model that can create symbolic lyrics and melodies following instructions.
Outcome: The proposed model outperforms existing models in symbolic song composition tasks.
An Effective Approach to Unsupervised Machine Translation (P19-1)

Copied to clipboard

Challenge: a recent research line has managed to train both unsupervised and unsupervised machine translation systems using monolingual corpora only.
Approach: They propose to use monolingual corpora to train both unsupervised and unsupervised machine translation systems.
Outcome: The proposed system achieves 22.5 BLEU points in English-to-German WMT 2014, 5.5 points more than the previous best unsupervised system, and 0.5 points more in the (supervised) shared task winner back in 2014.
Unsupervised Melody-to-Lyrics Generation (2023.acl-long)

Copied to clipboard

Challenge: Existing methods for automatic melody-to-lyric generation are limited due to the limited amount of melody-lyrical aligned data.
Approach: They propose a method for automatic melody-to-lyric generation without training on any aligned melody-lyr data.
Outcome: The proposed model generates high-quality lyrics that are singable, intelligible, and coherent than baseline models.
Generative Music Models’ Alignment with Professional and Amateur Users’ Expectations (2025.findings-acl)

Copied to clipboard

Challenge: Recent years have witnessed rapid advances in text-to-music generation using large language models.
Approach: They propose a task to align AI-generated music with human expressions . they use a dataset of over 1.5 million songs to analyze their content .
Outcome: The proposed framework outperforms baseline models and facilitates end-to-end generation of songs audio.
Encouraging Lexical Translation Consistency for Document-Level Neural Machine Translation (2021.emnlp-main)

Copied to clipboard

Challenge: Experimental results show document-level neural machine translation improves lexical consistency . inconsistent translations tend to confuse readers in some cases .
Approach: They propose to use a word link to obtain a document word link and an auxiliary loss function to constrain that their translation should be consistent.
Outcome: The proposed approach improves translation consistency on ChineseEnglish and EnglishFrench translation tasks.
Examining the Tip of the Iceberg: A Data Set for Idiom Translation (L18-1)

Copied to clipboard

Challenge: Neural Machine Translation (NMT) has been widely used in recent years with significant improvements for many language pairs.
Approach: They propose to use a large-scale data set to evaluate idiom translation in GermanEnglish.
Outcome: The proposed dataset is used to perform preliminary NMT experiments on idiom translation in GermanEnglish.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations