39 research outputs found
Break it Down for Me: A Study in Automated Lyric Annotation
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.Comment: To appear in Proceedings of EMNLP 201
Neural Networks for Information Retrieval
Machine learning plays a role in many aspects of modern IR systems, and deep
learning is applied in all of them. The fast pace of modern-day research has
given rise to many different approaches for many different IR problems. The
amount of information available can be overwhelming both for junior students
and for experienced researchers looking for new research topics and directions.
Additionally, it is interesting to see what key insights into IR problems the
new technologies are able to give us. The aim of this full-day tutorial is to
give a clear overview of current tried-and-trusted neural methods in IR and how
they benefit IR research. It covers key architectures, as well as the most
promising future directions.Comment: Overview of full-day tutorial at SIGIR 201