## 4 Experimental Validation
### Experimental Results
Table 3 presents the performance in terms of the precision, recall, and F-measure metrics for the models trained with a three-layer feedforward generator and the discriminator networks, consisting of a single linear layer (encoder) and a linear transformation back to the vocabulary size (decoder), respectively. We observe that the sentence-level Movie Reviews Dataset performs better, while the document-level 20 Newsgroups dataset provides slightly better precision results compared to the Finegrained sentences. To visualize our produced results, we further created the precision-recall curves and we treated our model's performance as a retrieval task on the 20 Newsgroups Dataset, on the Movie Reviews Dataset, and on the FSD. The precision-recall curves for the recall values given in Table 3 are presented in Figure 3.  
Figure 3: Precision–recall curves of our proposed GAN model on the 20 Newsgroups, the Movie Reviews, and the Finegrained datasets. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{3}{c}{**Baseline**} & \multicolumn{3}{c}{**Proposed GAN**} \\ \hline Dataset & Precision & Recall & F-measure & Precision & Recall & F-measure \\ \hline \multirow{4}{*}{20 Newsgroups} & 0.2521 & 0.0001 & 0.996 \(\times\) 10\({}^{-4}\) & 0.4188 & 0.0001 & 1.999 \(\times\) 10\({}^{-4}\) \\ \cline{2-7}  & 0.2099 & 0.0002 & 3.996 \(\times\) 10\({}^{-4}\) & 0.4012 & 0.0002 & 3.996 \(\times\) 10\({}^{-4}\) \\ \cline{2-7}  & 0.1005 & 0.0005 & 9 \(\times\) 10\({}^{-4}\) & 0.3648 & 0.0005 & 9.986 \(\times\) 10\({}^{-4}\) \\ \hline \multirow{4}{*}{Movie Reviews} & 0.3637 & 0.0001 & 1.999 \(\times\) 10\({}^{-4}\) & 0.6376 & 0.0001 & 1.999 \(\times\) 10\({}^{-4}\) \\ \cline{2-7}  & 0.3637 & 0.0002 & 3.978 \(\times\) 10\({}^{-4}\) & 0.6376 & 0.0002 & 3.998 \(\times\) 10\({}^{-4}\) \\ \cline{2-7}  & 0.3901 & 0.0005 & 9.871 \(\times\) 10\({}^{-4}\) & 0.6202 & 0.0005 & 9.991 \(\times\) 10\({}^{-4}\) \\ \hline \multirow{4}{*}{Finegrained Dataset (FSD)} & 0.1022 & 0.0001 & 2.101 \(\times\) 10\({}^{-4}\) & 0.3522 & 0.0001 & 2 \(\times\) 10\({}^{-4}\) \\ \cline{2-7}  & 0.1152 & 0.0002 & 3.993 \(\times\) 10\({}^{-4}\) & 0.3483 & 0.0002 & 3.997 \(\times\) 10\({}^{-4}\) \\ \cline{2-7}  & 0.3185 & 0.0005 & 9 \(\times\) 10\({}^{-4}\) & 0.3483 & 0.0005 & 9.985 \(\times\) 10\({}^{-4}\) \\ \hline \end{tabular}
\end{table} 
Table 3: Our proposed GAN model’s performance as a generative model when using various amounts and types of data at the document, sentence, and aspect level, compared with a simple GAN, which serves as our baseline. Figure 4 shows the visualizations of the learned representations created using the t-Distributed Stochastic Neighbor Embedding (t-SNE) toolkit [74]. We used this toolkit and created 2D projections of the data distribution. Particularly, Figure 4a-c show the distributions of the test document-, sentence-, and aspect-level test data learned by our proposed adversarial model, respectively. The documents belong to 20 different topics, the sentences of the Movie Reviews dataset belong to 2 different categories, while the sentences of the Finegrained dataset belong to 5 categories, with separate categories corresponding to different colored points in the subfigures. By using adversarial training, the feature distributions for the test samples from all three datasets are almost indistinguishable, demonstrating that our proposed approach is able to find common representation. Figure 4: t-Distributed Stochastic Neighbor Embedding (t-SNE) visualizations of the document, sentence, and aspect representations learned by our proposed adversarial model on the three test datasets. The color indicates different class labels. (**a**) Document-level representation using the GAN model; (**b**) Sentence-level representation using the GAN model; (**c**) Aspect-level representation using the GAN model.