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Annotating music items with music genres is crucial for music recommendationand information retrieval, yet challenging given that music genres aresubjective concepts. Recently, in order to explicitly consider thissubjectivity, the annotation of music items was modeled as a translation task:predict for a music item its music genres within a target vocabulary ortaxonomy (tag system) from a set of music genre tags originating from other tagsystems. However, without a parallel corpus, previous solutions could nothandle tag systems in other languages, being limited to the English-languageonly. Here, by learning multilingual music genre embeddings, we enablecross-lingual music genre translation without relying on a parallel corpus.First, we apply compositionality functions on pre-trained word embeddings torepresent multi-word tags.Second, we adapt the tag representations to the musicdomain by leveraging multilingual music genres graphs with a modifiedretrofitting algorithm. Experiments show that our method: 1) is effective intranslating music genres across tag systems in multiple languages (English,French and Spanish); 2) outperforms the previous baseline in anEnglish-language multi-source translation task. We publicly release the newmultilingual data and code.

Multilingual Music Genre Embeddings for Effective Cross-Lingual Music Item Annotation