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 . Song lyrics and poetry often make use of ambiguity , symbolism , irony , and other stylistic elements to evoke emotive responses . These characteristics sometimes make it challenging to interpret obscure lyrics , especially for readers or listeners who are unfamiliar with the genre . To address this problem , several online lyric databases have been created where users can explain , contextualize , or discuss lyrics . Examples include MetroLyrics1 and Genius.com2 . We refer to such How does it feel ? To be without a home Like a complete unknown , We presented and released the Genius dataset to study the task of Automated Lyric Annotation . As a first investigation , we studied automatic generation of context independent annotations as machine translation and information retrieval . Our baseline system tests indicate that our corpus is suitable to train machine translation systems . Standard SMT models are capable of rephrasing and simplifying song lyrics but tend to keep close to the structure of the song lyric . Seq2Seq models demonstrated potential to generate more fluent and informative text , dissimilar to the lyric . A large fraction of the annotations is heavily based on context and background knowledge ( CS ) , one of their most appealing aspects . As future work we suggest injection of structured and unstructured external knowledge ( Ahn et al . , 2016 ) and explicit modeling of references ( Yang et al . , 2016 ) .

Challenge: Because of its creative nature, understanding lyrics can be challenging both for humans and machines.
Approach: They propose a task of automated lyric annotation with a dataset collected from an online platform which explains lyrics to readers.
Outcome: They evaluate translation and retrieval models with automatic and human evaluation and show that different models capture different aspects well.