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