Determining if the lyrics of a given song could be hurtful or inappropriate for children is of utmost importance to prevent the reproduction of songs whose textual content is unsuitable for them. This problem can be computationally tackled as a binary classification task, and in the last couple of years various machine learning approaches have been applied to perform this task automatically. In this work, we investigate the automatic detection of explicit song lyrics by leveraging transformer-based language models, i.e., large language representations, unsupervisely built from huge textual corpora, that can be fine-tuned on various natural language processing tasks, such as text classification. We assess the performance of various transformer-based language model classifiers on a dataset consisting of more than 800K lyrics, marked with explicit information. The evaluation shows that while the classifiers built with these powerful tools achieve state-of-the-art performance, they do not outperform lighter and computationally less demanding approaches. We complement this empirical evaluation with further analyses, including an assessment of the performance of these classifiers in a few-shot learning scenario, where they are trained with just few thousands of samples.

On exploiting transformers for detecting explicit song lyrics

Rospocher, M
2022-01-01

Abstract

Determining if the lyrics of a given song could be hurtful or inappropriate for children is of utmost importance to prevent the reproduction of songs whose textual content is unsuitable for them. This problem can be computationally tackled as a binary classification task, and in the last couple of years various machine learning approaches have been applied to perform this task automatically. In this work, we investigate the automatic detection of explicit song lyrics by leveraging transformer-based language models, i.e., large language representations, unsupervisely built from huge textual corpora, that can be fine-tuned on various natural language processing tasks, such as text classification. We assess the performance of various transformer-based language model classifiers on a dataset consisting of more than 800K lyrics, marked with explicit information. The evaluation shows that while the classifiers built with these powerful tools achieve state-of-the-art performance, they do not outperform lighter and computationally less demanding approaches. We complement this empirical evaluation with further analyses, including an assessment of the performance of these classifiers in a few-shot learning scenario, where they are trained with just few thousands of samples.
Transformer-based language models
Convolutional neural networks
Text classification
Explicit content detection
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/1072686
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