# Multi-Task Aspect-Based Sentiment: A Hybrid Sampling and Stance Detection Approach
## 7 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Asthma**} & \multicolumn{4}{c}{**Obesity**} \\ \cline{2-9}  & **SE-F1** & **ASC-F1** & **ATE-F1** & **ABSA-F1** & **SE-F1** & **ASC-F1** & **ATE-F1** & **ABSA-F1** \\ \hline IMN-GloVe & 82.0\% & 84.0\% & 87.3\% & 88.67\% & 86.9\% & 88.56\% & 85.5\% & **93.3\%** \\ BERT-Single & 85.14\% & 87.25\% & **90.14\%** & 90.07\% & 86.87\% & 87.98\% & 86.68\% & **92.06\%** \\ AEN-BERT & 88.3\% & 89.9\% & **93.83\%** & 80.74\% & 88.08\% & 90.0\% & 92.28\% & **93.93\%** \\ BERT-SPC & 89.9\% & 91.6\% & **93.3\%** & 90.11\% & 88.83\% & 88.55\% & 92.51\% & **93.89\%** \\ LCF-BERT & 88.7\% & 91.1\% & **92.8\%** & 91.72\% & 89.3\% & 89.86\% & 93.6\% & **94.4\%** \\ BERT-Pair & 91.37\% & 91.1\% & 92.67\% & **93.60\%** & 88.7\% & 86.82\% & 92.1\% & **92.4\%** \\ MMS (our) & 87.85\% & 89.05\% & 95.94\% & **96.4\%** & **94.45\%** & 86.64\% & 89.35\% & 93.0\% \\ MMS (10\%) & 87.92\% & 84.61\% & 85.26\% & **92.74\%** & **91.04\%** & 86.24\% & 88.58\% & 86.4\% \\ MMS (20\%) & 91.82\% & 89.25\% & 89.87\% & **93.76\%** & **92.78\%** & 89.07\% & 90.89\% & 89.05\% \\ MMS (40\%) & 92.07\% & 90.86\% & 91.25\% & **93.39\%** & **92.78\%** & 90.24\% & 91.54\% & 89.35\% \\ MMS (70\%) & **93.78\%** & 91.37\% & 91.77\% & 90.07\% & 93.04\% & 89.95\% & 91.47\% & **93.60\%** \\ MMS (100\%) & 91.37\% & 91.77\% & 86.82\% & **96.90\%** & 93.72\% & 93.78\% & 93.43\% & **94.76\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The comparative results of the aspect category polarity on Asthma and Obesity datasets were measured by the F1 metric with multi-sub tasks. Some results of the state-of-the-art methods are retrieved from the original papers, while the others are our implementation. The best scores are highlighted in bold. fewer pseudo-labels and focuses on re-weighting data instances in \(D_{mix}\). Then, the final improvement of MMS is 1.41% compared with the BERT-Pair method in Obesity and 0.84% with LCF-BERT in Asthma. More labeled data for MMS, all MMS F1 scores of distinct sub-tasks are generally growing. With only 20% labeled data from ABSA-F1, this paper suggested MMS beats IMN-GloVe in all sub-tasks. In the same settings, MMS is comparable to the performance of LCF-BERT in Asthma and Obesity datasets, achieving improved results in Asthma. By applying the same BERT encoder and 40% labeled data, MMS can attain the enhancements of around 6% ABSA-F1 for Asthma and approximately 1% ABSA-F1 for Obesity, compared with the state-of-the-art methods. Following all baselines, this paper considers the first sentiment label predicted for an aspect phrase to be an ASC result, and this paper excludes conflicting sentiment labels from the ASC subtask. This exclusion may cause the ratio sum in ASC to be less than 100%. Additionally, as mentioned earlier, this paper assigns an enhanced dataset for each dataset to further investigate MMS's data usage. On the other hand, the effects of the quantity of data with and without labels. This paragraph investigates the effects of various amounts of annotated data on MMS. The impacts of incorporating and removing unlabeled data on model performance are first contrasted (see Table 5). All models often demonstrate the growing F1 measures by using additional labeled data. Across all of the used labeled data ratios, MMS (including unlabeled data) may consistently enhance the ABSA-F1 measurements by 10% to 40%. The largest discrepancy between MMS labeled data and unlabeled data emerges when employing 10% labeled data in Asthma, accounting for 4.10%. The greatest noticeable gains for Obesity are provided by 1.10% with 40% labeled data. MSM with 10% tagged data shows a modest improvement for the obesity and asthma datasets. The improvement is visible when utilizing more than 20% tagged data. This is due to the difficulty in managing a workable instructor model with only 10% labeled data. Effective initialization of the instructor model is required for MMS to prevent progressive drift.