Whether consciously or inadvertently, our messages can include toxic language which contributes to the polarization of social networks. Intelligent techniques can help us detect these expressions and even change them into kinder expressions by applying style transfer techniques.
This work aims to advance detoxification style transfer techniques using deep learning and semantic similarity technologies. The article explores the advantages of a toxicity-deletion method that uses linguistic resources in a detoxification system. For this purpose, we propose a method that removes toxic words from the source sentence using a similarity function with a toxic vocabulary. We present two models that leverage it, namely, LexiconGST and MultiLexiconGST, which are based on the
Delete –Retrieve–Generate framework. Experimental results show that our models perform well in the detoxification task compared to other state-of-the-art methods. Finally, this research confirms that linguistic resources can guide deep learning techniques and improve their performance.