Break it Down for Me: A Study in Automated Lyric Annotation

“Break it Down for Me: A Study in Automated Lyric Annotation” by Lucas Sterckx, Jason Naradowsky, Thomas Demeester, William Byrne, and Chris Develder. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Demo Papers), 2017.

Abstract

Comprehending lyrics, as found in songs and poems, can pose a challenge to human and machine readers alike. This motivates the need for systems that can understand the ambiguity and jargon found in such creative texts, and provide commentary to aid readers in reaching the correct interpretation. We introduce the task of automated lyric annotation (ALA). Like text simplification, a goal of ALA is to rephrase the original text in a more easily understandable manner. However, in ALA the system must often include additional information to clarify niche terminology and abstract concepts. To stimulate research on this task, we release a large collection of crowdsourced annotations for song lyrics. We analyze the performance of translation and retrieval models on this task, measuring performance with both automated and human evaluation. We find that each model captures a unique type of information important to the task.

BibTeX entry:

@inproceedings{emnlp17:breakitdownforme,
   author = {Lucas Sterckx and Jason Naradowsky and Thomas Demeester and
	William Byrne and Chris Develder},
   title = {Break it Down for Me: A Study in Automated Lyric Annotation},
   booktitle = {Proceedings of the 2017 Conference on Empirical Methods in
	Natural Language Processing (Demo Papers)},
   year = {2017}
}

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