LyricSIM: A novel Dataset and Benchmark for Similarity Detection in
  Spanish Song LyricS
Alejandro Benito-Santos Adrián Ghajari Pedro Hernández Víctor Fresno Salvador Ros Elena González-Blanco
In this paper, we present a new dataset and benchmark tailored to the task of
semantic similarity in song lyrics. Our dataset, originally consisting of 2775
pairs of Spanish songs, was annotated in a collective annotation experiment by
63 native annotators. After collecting and refining the data to ensure a high
degree of consensus and data integrity, we obtained 676 high-quality annotated
pairs that were used to evaluate the performance of various state-of-the-art
monolingual and multilingual language models. Consequently, we established
baseline results that we hope will be useful to the community in all future
academic and industrial applications conducted in this context.