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

Lucas Sterckx, Jason Naradowsky, Bill Byrne, Thomas Demeester, Chris Develder


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.
Anthology ID:
D17-1220
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2074–2080
Language:
URL:
https://www.aclweb.org/anthology/D17-1220
DOI:
10.18653/v1/D17-1220
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1220.pdf