## 5 Results  
To analyze the performance of the models, we focused attention on the aggregated metric J. More specifically, we wanted to evaluate if the general style transfer method proposed by [PERSON] et al. [19] is valid for the detoxification task and if its model can be improved by changing its style deletion system from attention-based to lexicon-based. Additionally, the effects of different similarity functions and lexicons were analyzed. In this sense, it is important to consider that the evaluation is limited by the accuracy of the pre-trained toxic and language acceptability classifiers, as well as the performance of the model that compares sentence embeddings. Lastly, we selected the different thresholds used in the experiments. To remove toxic words based on their lexicon similarity, we picked \(similarity\_thres=0.7\) after tuning the parameters. On the contrary, for the threshold that determines whether a word is included in our dataset-specific lexicon, we chose a value that produces a lexicon size similar to the lexicons we were working with, i.e., for Jigsaw \(toxicity\_thres=0.8\) and for ParaDetox \(toxicity\_thres=0.7\). Firstly, we evaluated LexiconGST and MultiLexiconGST on multiple lexicons and used different average top-\(K\) similarity functions to find that performance depends a lot on the lexicon and the value of \(K\). However, we discovered that, regardless of the lexicon, the best \(K\) are \(\{1,3,5\}\) by performing a grid search on the validation split. Specifically, Figure 2 shows how similarity functions that consider \(K>5\) words for average similarity always lead to a significant decrease in performance in contrast to when \(K<5\) words are considered. We also observe from this wide evaluation how the fluency (FL) and source similarity tend to increase as the style transfer strength (ACC) decreases; our intuition is that the smaller the change in style, the fewer the words that are changed from the input, which leads to a more fluent and similar sentence to the input. Secondly, in Table 3, we present our best-performing models for each lexicon and show that both MultiLexiconGST and LexiconGST outperform BlindedGST [19]. The proposed dataset-specific lexicons almost always perform best, while the Hurtlex lexicon performs the worst. More specifically, when MultiLexiconGST makes use of the dataset-specific lexicons, it yields the best J score since it is capable of decently changing the style (ACC) while maintaining high fluency (FL) thanks to the leverage of the extra lexicon Stopwords-en, which we believe allows it to be more accurate when deleting toxic words. Concretely, the biggest increase in quality (\(\uparrow 0.18\)) over BlindedGST is in style transfer strength (ACC) for the Jigsaw dataset. In contrast, an improvement (\(\uparrow 0.19\)) in fluency (FL) makes the difference for the ParaDetox dataset. In order to further study the impact of the proposed methods and their performance, we conducted the Friedman statistical test [50]. This test computes a sorted ranking of approaches, offering an aggregated view of their performance across all datasets. In this way, a lower ranking indicates a better result for a certain method in comparison to the rest. Ties are resolved by averaging the ranks obtained. We performed the Friedman test with \(\alpha=0.05\), rejecting the null hypothesis. In Table 4, it can be seen that the best model, as indicated by the Friedman test, is the MultiLexiconGST using a dataset-specific lexicon. This reinforces the observation that using domain-oriented resources leads to a performance improvement, and is consistent with our initial hypotheses. In comparison, BlindedGST is placed at the bottom of the ranking, which again indicates that introducing relevant linguistic resources effectively improves the style transfer process. Finally, we compared our methods with the state-of-the-art (SOTA) models shown in Table 5. To obtain a fair comparison of the methods, we left out all methods with a nontrivial \"Retrieval\" component (Delete-Retrieve-Generate).