## 4 Experimental Design
### Resources  
To train and evaluate the performance of our style transfer model, we used two datasets that allowed us to compare our results to other works and make our results significant: (i) the English dataset from the previous Jigsaw [37] competition since it is widely used among the most recent detoxification literature [9; 30]; and the (ii) ParaDetox dataset [32], which was recently released and designed specifically for the task of detoxification. Since the Jigsaw dataset has no parallel (source-to-target) sentences, we generated a neutral and toxic corpus separately. To construct the toxic corpus, the comments labeled as toxic were first split into individual sentences as the original comments may be too lengthy. Subsequently, each sentence was classified using the RoBERTa-based toxicity classifier from [PERSON] et al. [30], which was trained on English data from the three Jigsaw datasets [37; 38; 39]. The toxic half of the dataset consists of sentences classified as toxic by the toxicity classifier, resulting in around 140,000 sentences (see Table 1). An equal number of nontoxic sentences were randomly selected from the sentence-separated Jigsaw data to construct the neutral half of the dataset. The test set was constructed using a similar approach to the Jigsaw competition, where 10,000 sentences with the highest toxicity scores were selected based on the toxicity classifier. On the other hand, ParaDetox is a parallel detoxification dataset collected from the large ParaNMT [33] dataset with the use of a crowd-sourced quality control pipeline. It consists of over 12,000 toxic sentences, each one having 1 to 3 neutral paraphrases. To build the toxic corpus, we just used the toxic sentences, and for the neutral corpus, we merged all available neutral sentences, which resulted in around 20,000 neutral sentences.  
We used the lexicons shown in Table 2 to experiment with our models, where five different lexicons were for toxic style deletion, and one lexicon was for the preservation of common and/or stopwords in MultiLexiconGST. The Abusive, Hurtlex, and Orthrus lexicons were taken from other works whose whole purpose was to craft such a lexicon. The other two lexicons, ParaDetox-Lex and Jigsaw-Lex, are a contribution of this work. We refer to them as dataset-specific lexicons since they were crafted specifically for each dataset, Jigsaw or Paradetox. To generate these lexicons, we first trained, for each dataset, a logistic regression toxicity classifier that approximates the weights **W** and biases **B** for the model \(Y=\textbf{WX}+\textbf{B}\), where \(Y\) is either toxic or nontoxic and **X** is a vector representing the words in a sentence. Then, we interpreted the weights **W** given by the logistic regressor to each word as a toxicity score. Finally, we used a threshold **t** for this toxicity score \(w_{word}\) to select which words are included in the lexicon if \(w_{word}>t\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Style** & **Train** & **Dev** & **Test** & **Total** \\ \hline \multirow{2}{*}{ParaDetox [32]} & Toxic & 11,351 & 596 & 671 & 12,618 \\  & Neutral & 19,274 & 491 & 671 & 20,436 \\ \hline \multirow{2}{*}{Jigsaw [37]} & Toxic & 135,390 & 6648 & 10,000 & 152,038 \\  & Neutral & 135,390 & 6648 & 10,000 & 152,038 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of instances per style and split of used datasets.