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

Lucas Sterckx, Jason Naradowsky, Bill Byrne, Thomas Demeester, Chris Develder
2017 Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing  
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
more » ... 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.
doi:10.18653/v1/d17-1220 dblp:conf/emnlp/SterckxNBDD17 fatcat:34jlw35k3fcgjb7q6bzp6nyv2i