## 6 Conclusions and Future Work  
In this paper, we introduce a novel algorithm for toxic style removal and present two models that leverage it, LexiconGST and MultiLexiconGST, which are based on the DRG framework. The experimental results show that our models have competitive performance in the detoxification task among the state-of-the-art methods. Specifically, our proposed methods outperform all SOTA models, which are based on the same framework. Furthermore, this research also confirms that linguistic resources can guide deep learning and improve its performance. We have also shown that the hyperparameter \(K\) helps the similarity function to adapt to the characteristics of the different lexicons. The proposed models focus on the Delete and Generate phases, which constitutes a limitation of our models. Indeed, including a mechanism that implements a nontrivial \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Model** & **Lexicon** & & & & & \\ \hline MultiLexiconGST & Dataset-specific & & & & & & \\ MultiLexiconGST & Abusive & & & & & \\ MultiLexiconGST & Orthrus & & & & & \\ LexiconGST & Orthrus & & & & & \\ LexiconGST & Abusive & & & & & \\ LexiconGST & Dataset-specific & & & & & \\ MultiLexiconGST & - & & & & & \\ BlindedGST & - & & & & & \\ LexiconGST & - & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: [PERSON] rank of the best 9 methods. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & **Model** & **Lexicon** & **K** & **ACC** & **SIM** & **FL** & **J** & **BLEU** \\ \hline \multirow{6}{*}{Paradetox} & \multirow{6}{*}{Lexicon} & Abusive & 5 & 0.70 & 0.89 & 0.75 & 0.47 & 0.77 \\  & & Hurtlex & 3 & 0.48 & 0.92 & 0.79 & 0.35 & 0.82 \\  & & Ofhrtlrus & 1 & 0.78 & 0.85 & 0.69 & 0.47 & 0.73 \\  & & Dataset-specific & 1 & 0.81 & 0.83 & 0.60 & 0.42 & 0.70 \\ \cline{2-10}  & & \multirow{2}{*}{MultiLexicon} & Abusive & 1 & 0.80 & 0.83 & 0.71 & 0.48 & 0.72 \\  & & Hurtlex & 1 & 0.65 & 0.89 & 0.77 & 0.45 & 0.78 \\  & & \multirow{2}{*}{GST} & _Ortlrus_ & 5 & 0.63 & 0.89 & 0.83 & 0.47 & 0.77 \\  & & Dataset-specific & 3 & 0.71 & 0.89 & 0.75 & **0.49** & 0.77 \\ \hline \multirow{6}{*}{Paradetox} & \multirow{6}{*}{Lexicon} & Abusive & 3 & 0.52 & 0.85 & 0.66 & 0.30 & 0.75 \\  & & Hurtlex & 1 & 0.45 & 0.86 & 0.61 & 0.24 & 0.76 \\  & & Orthrus & 3 & 0.75 & 0.80 & 0.55 & 0.32 & 0.69 \\  & & Dataset-specific & 3 & 0.78 & 0.77 & 0.58 & 0.35 & 0.69 \\ \cline{2-10}  & & Abusive & 1 & 0.64 & 0.80 & 0.65 & 0.34 & 0.72 \\  & & Hurtlex & 1 & 0.43 & 0.87 & 0.70 & 0.27 & 0.78 \\  & & GST & Orthrus & 3 & 0.66 & 0.82 & 0.67 & 0.37 & 0.74 \\  & & _Dataset-specific_ & 3 & 0.73 & 0.79 & 0.64 & **0.38** & 0.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the best-performing models for each lexicon on the test splits of the Jigsaw and Paradetox datasets. Best J results are indicated by bold numbers. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Lexicon** & & **Rank** \\ \hline MultiLexiconGST & Dataset-specific & & 1.0 \\ MultiLexiconGST & Abusive & & 3.0 \\ MultiLexiconGST & Orthrus & & 3.0 \\ LexiconGST & Orthrus & & 4.5 \\ LexiconGST & Abusive & & 5.0 \\ LexiconGST & Dataset-specific & & 5.0 \\ MultiLexiconGST & Hurtlex & & 6.75 \\ BlindedGST & - & & 8.25 \\ LexiconGST & Hurtlex & & 8.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: SOTA performances of methods that use a Delete and Generate framework. Best J results are indicated by bold numbers. Retrieve phase can enhance the overall performance of the system. As future work, this can be addressed by evaluating whether the proposed models can outperform SOTA methods that use a more complex Retrieve step like CondBERT and ParaGeDi [30]. Furthermore, the proposed methods use lexicons as a source of domain-centered knowledge sources. Nevertheless, deriving these lexicons and evaluating their quality are open challenges, which is a limitation of this work. This opens new avenues of research, oriented to generating and exploiting lexicons to improve the results of the task.