Annotating music items with music genres is crucial for music recommendation and information retrieval, yet challenging given that music genres are subjective concepts. Recently, in order to explicitly consider this subjectivity, the annotation of music items was modeled as a translation task: predict for a music item its music genres within a target vocabulary or taxonomy (tag system) from a set of music genre tags originating from other tag systems. However, without a parallel corpus, previous solutions could not handle tag systems in other languages, being limited to the English-language only. Here, by learning multilingual music genre embeddings, we enable cross-lingual music genre translation without relying on a parallel corpus. First, we apply compositionality functions on pre-trained word embeddings to represent multi-word tags.Second, we adapt the tag representations to the music domain by leveraging multilingual music genres graphs with a modified retrofitting algorithm. Experiments show that our method: 1) is effective in translating music genres across tag systems in multiple languages (English, French and Spanish); 2) outperforms the previous baseline in an English-language multi-source translation task. We publicly release the new multilingual data and code.

中文翻译：

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用于有效跨语言音乐项目注释的多语言音乐流派嵌入

用音乐流派注释音乐项目对于音乐推荐和信息检索至关重要，但鉴于音乐流派是主观概念，因此具有挑战性。最近，为了明确考虑这种主观性，音乐项目的注释被建模为一项翻译任务：从一组源自其他音乐类型的音乐类型标签中预测目标词汇或分类法（标签系统）中的音乐项目的音乐类型。标签系统。然而，如果没有平行语料库，以前的解决方案无法处理其他语言的标签系统，仅限于英语。在这里，通过学习多语言音乐流派嵌入，我们可以在不依赖平行语料库的情况下实现跨语言音乐流派翻译。首先，我们在预训练的词嵌入上应用组合函数来表示多词标签。第二，我们通过利用多语言音乐流派图和修改后的改造算法使标签表示适应音乐领域。实验表明，我们的方法：1）在跨标签系统以多种语言（英语、法语和西班牙语）翻译音乐流派方面是有效的；2) 在英语多源翻译任务中优于之前的基线。我们公开发布新的多语言数据和代码。