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.

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