@InProceedings{sterckx-EtAl:2017:EMNLP2017,
  author    = {Sterckx, Lucas  and  Naradowsky, Jason  and  Byrne, Bill  and  Demeester, Thomas  and  Develder, Chris},
  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},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {2074--2080},
  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.},
  url       = {https://www.aclweb.org/anthology/D17-1220}
}