LyricSIM: A novel Dataset and Benchmark for Similarity Detection in Spanish Song LyricS
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Abstract (arXiv)
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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References · 12
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- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,107
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- 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,171
- 2017SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluationcited by 998
- 2020Evolution of Semantic Similarity -- A Surveycited by 83
- 2022BERTIN: Efficient Pre-Training of a Spanish Language Model using Perplexity Samplingcited by 30
- 2020PO-EMO: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in German and English Poetrycited by 17
- 2021CCPM: A Chinese Classical Poetry Matching Datasetcited by 15
- 2022Lessons learned from the evaluation of Spanish Language Modelscited by 8
- 2021Optimal Embedding Calibration for Symbolic Music Similaritycited by 1
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