@inproceedings{sterckx-etal-2017-break,
title = "Break it Down for Me: A Study in Automated Lyric Annotation",
author = "Sterckx, Lucas and
Naradowsky, Jason and
Byrne, Bill and
Demeester, Thomas and
Develder, Chris",
editor = "Palmer, Martha and
Hwa, Rebecca and
Riedel, Sebastian",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D17-1220",
doi = "10.18653/v1/D17-1220",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Break it Down for Me: A Study in Automated Lyric Annotation
%A Sterckx, Lucas
%A Naradowsky, Jason
%A Byrne, Bill
%A Demeester, Thomas
%A Develder, Chris
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F sterckx-etal-2017-break
%X 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.
%R 10.18653/v1/D17-1220
%U https://aclanthology.org/D17-1220
%U https://doi.org/10.18653/v1/D17-1220
%P 2074-2080
Markdown (Informal)
[Break it Down for Me: A Study in Automated Lyric Annotation](https://aclanthology.org/D17-1220) (Sterckx et al., EMNLP 2017)
ACL
- Lucas Sterckx, Jason Naradowsky, Bill Byrne, Thomas Demeester, and Chris Develder. 2017. Break it Down for Me: A Study in Automated Lyric Annotation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2074–2080, Copenhagen, Denmark. Association for Computational Linguistics.