## 6 Conclusions  
In this study, we introduce an SIWLM scheme consisting of SWL and IWL to solve a model confusion problem in MTL in NLP. With this scheme, the model can thoroughly learn all tasks while still strengthening its understanding of the central task. In comparing our model with the MT-DNN and SMART models, our model demonstrated the highest performance for the GLUE benchmark tasks excluding CoLA and STS-B. This result confirms that the SIWLM scheme contributes to the performance improvement of the MTL model. Furthermore, based on experiments using various weights, when the data size is large, the performance improves more when the weight of the central task is less than 6. Conversely, when the data size is small, the performance is improved even more when a weight of 9 or more is applied. Moreover, we find that the performance variance is large in the small data size task, whereas the weight application method is more effective under \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **MNLI-m/mm** & **QQP Acc/F1** & **QNLI Acc** & \begin{tabular}{c} **SST-2** \\ **Acc** \\ \end{tabular} & **CoLA Mcc** & \
\begin{tabular}{c} **STS-B** \\ **P/S Corr** \\ \end{tabular} &
\begin{tabular}{c} **MRPC** \\ **Acc/F1** \\ \end{tabular} & **RTE Acc** \\ \hline MT-DNN [4] & 84.2/83.0 & 71.4/89.1 & 90.9 & 93.1 & 50.1 & **87.4/86.8** & 86.4/89.8 & 75.5 \\ SMART [7] & 84.7/83.2 & **89.5/71.6** & **91.7** & 92.5 & **55.2** & 82.6/81.0 & 86.6/89.8 & 72.7 \\ MTDNN-SIWLM & **84.8/84.0** & **89.4/71.7** & **91.7** & **93.8** & 53.1 & 83.8/82.3 & **90.7/87.6** & **77.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Fine-tuned results for eight tasks of GLUE. The best result is in bold font and the second-best result is underlined. For two metrics, we attain the highest performance model by averaging two performances. These GLUE test set results were measured using the GLUE evaluation server on 19 January 2021. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **MNLI-m/mm Acc** & **QQP Acc** & **QNLI Acc** & **SST-2 Acc** & **CoLA Mcc** & **STS-B** & **Avg** & **MRPC Acc** & **RTE Acc** \\ \hline BERT [11] & 84.1 & 90.8 & 91.2 & 93.0 & 52.8 & 83.7 & 86.5 & 67.1 \\ BAM [5] & **84.9** & 91.0 & 91.4 & 92.8 & 56.3 & 86.9 & 88.5 & **82.7** \\ MT-DNN [4] & 84.0 & 89.5 & 91.4 & 92.1 & 53.1 & **89.6** & 89.7 & 78.7 \\ SMART [7] & **84.9** & 91.4 & 91.8 & 91.6 & **58.5** & 85.7 & 87.2 & 79.7 \\ MTDNN-SIWLM & 84.8 & **91.5** & **92.0** & **93.2** & 57.0 & 86.7 & **90.4** & 79.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Validation performance of reproduced models and MTDNN-SIWLM. The best result is in bold font and the second-best result is underlined. For STS-B, we attain the performance by averaging the Pearson and Spearman correlations. such a condition. The proposed SIWLM-based learning scheme can be applied to other MTL models to improve learning efficiency and prediction accuracy, especially for tasks with small datasets. While our approach outperforms the MT-DNN model in most GLUE datasets, there are still several possible directions for improving our model. We searched optimal weights for each task through several experiments. However, our optimization method is expensive, and we must find optimal weights when new tasks are added. Therefore, we seek to investigate a scheme that enables a general weight regardless of tasks. Future studies can apply our SIWLM scheme to MTL architectures in various fields [35, 36, 37] to improve their performance. Furthermore, additional training on other NLU datasets such as SuperGLUE [38] may help reinforce our model. [PERSON], [PERSON], [PERSON], and [PERSON] designed the method. [PERSON], [PERSON], and [PERSON] implemented the method and wrote the paper. [PERSON] and [PERSON] contributed to the supervision of the work, analysis of the method, and paper writing. All authors have read and agreed to the published version of the manuscript.