Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer

By C. D. Santos, Igor Melnyk, Inkit Padhi. 2018

170 citations recorded by the source corpus.

We introduce a new approach to tackle the problem of offensive language in online social media. Our approach uses unsupervised text style transfer to translate offensive sentences into non-offensive ones. We propose a new method for training encoder-decoders using non-parallel data that combines a collaborative classifier, attention and the cycle consistency loss. Experimental results on data from Twitter and Reddit show that our method outperforms a state-of-the-art text style transfer system in two out of three quantitative metrics and produces reliable non-offensive transferred sentences.

Subfields: Artificial Intelligence, Information Systems, Social Psychology, Natural Language Processing

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Citations in the corpus

Challenges in Detoxifying Language Models (2021, background): “Other work on toxicity in generated text includes Xu et al. (2020), who investigate safety specifically in a dialogue setting, and translating existing offensive text into non-offensive variants (Nogueira dos Santos et al., 2018; Laugier et al., 2021).”
Recipes for Safety in Open-domain Chatbots (2020, background): “Santos et al. (2018) use unsupervised style transfer to translate offensive sentences into innocuous ones.”
Challenges and frontiers in abusive content detection (2019, background): “Researchers have also addressed numerous tasks beyond binary abusive content classification, including identifying the target of abuse and its strength as well as automatically moderating content (Burnap & Williams, 2016; Davidson, Warmsley, Macy, & Weber, 2017; Santos, Melnyk, & Padhi, 2018).”
Disentangled Representation Learning for Non-Parallel Text Style Transfer (2018, unknown): “We set the maximum length of a sentence to 15 words and the vocabulary size to ∼9200, following Shen et al. (2017). Amazon Product Reviews.”
Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation (2019, methodology): “Most of the previous methods (Hu et al., 2017; Shen et al., 2017; Fu et al., 2018; Carlson et al., 2017; Zhang et al., 2018b,a; Prabhumoye et al., 2018; Jin et al., 2019; Melnyk et al., 2017; dos Santos et al., 2018) formulate the style transfer problem into the “encoder-decoder” framework.”
Civil Rephrases Of Toxic Texts With Self-Supervised Transformers (2021, methodology): “Unsupervised complex text attribute transfer (like civil rephrasing of toxic comments) remains in its early stages, and our particular applied task has only a single antecedent (Nogueira dos Santos et al., 2018).”
Deep Learning for Text Style Transfer: A Survey (2020, methodology): “For (1), we suggest that future works use at least one of the most commonly used benchmark datasets, such as the Yelp data prepreocessed by Shen et al. (2017) and its five human references provided by Jin et al.”
Text Style Transfer: A Review and Experiment Evaluation (2020, methodology): “Other TST studies have also adopted the back-translation technique originally proposed for NMT [65] to transfer styles in text [13, 62].”
Text Detoxification using Large Pre-trained Neural Models (2021, background): “The existing works on text detoxification (dos Santos et al., 2018; Tran et al., 2020; Laugier et al., 2021) cast this task as style transfer.”