## 5 Results and Discussion
### Ablation Study  
This section discusses the variants of neural networks to show the effectiveness of models on DSTC-2 and Woz-2.0 datasets in Table 5 to compare the results with the proposed BLA model. We compose BSL with the last hidden state of BERT for input to SBL and then concatenate the outputs of BERT and SBL. It decreases the joint goal accuracies on both datasets as well as the turn requests on the DSTC-2. In contrast, SBL has no effect on both datasets regarding turn goal accuracies. However, the turn request accuracy on Woz-2.0 dataset produces the highest results among all models. Later we used the combination of input to Stacked BiGRU (SBG) from the last hidden state of BERT, and then BERT and SBG output concatenation to formalize BSG. It produced the same results on Woz-2.0 dataset for turn goal accuracy but improves the turn goal accuracy as higher than all models. However, the accuracies for both datasets' joint goal and DSTC-2 turn goal decreased. Next, we used the last hidden state of BERT for SBL, then the hidden state of SBL to SBG to concatenate the outputs of BERT and SBG to design BLG. It produced the highest turn goal on DSTC-2; however, it lowered the accuracies of turn request and joint goal on DSTC-2, and turn goal, turn request, and joint goal on Woz-2.0. In BLC, we used the last hidden state of BERT to give input in SBL, and then all the hidden states of SBL for the conditional random field (CRF) to concatenate outputs of BERT and CRF. Next, we deployed BLCA similar to BLC with slightly difference in addition of multiple attentions over the overall hidden states of SBL and then the concatenation of BERT, CRF, and multiple attentions. We observed that the performance on all accuracies across both datasets poorly decreased for BLC and BLCA. The second last model BCLA in Table 5 produced insignificant results upon all accuracies than other models on Woz-2.0 datasets and decreased all accuracies for DSTC-2 as compared to the proposed model. BCLA is composed with the last hidden state of BERT as input to CNN, then the output of CNN embeds to SBL. Later, self-attention applies to the overall hidden states of SBL. Finally, BERT, SBL, and self-attention outputs were concatenated. * **Effect of Stacked BiLSTM Network** We used variants of neural networks to investigate the performance of BLA in the ablation study. We replaced Stacked BiLSTM with Stacked BiGRU and run the model on Dev and Test parts of the Woz-2.0 datasets. It clearly indicates that the joint goal accuracy on Dev, and joint goal, turn goal, and turn request accuracies on the Test part improve and utilize more accurate representations regarding SBL. * **Effect of Attention Mechanism** To investigate the effect of attention, we conduct multiple attention and self-attention variants with different models. In Table 5, we \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**DSTC-2**} & \multicolumn{2}{c}{**Woz-2.0**} \\ \cline{2-5}  & **Turn Request** & **Joint Goal** & **Turn Request** & **Joint Goal** \\ \hline NBT \({}^{\ddagger}\) & 97.50 & 73.40 & 91.60 & 84.20 \\ GLAD \({}^{\ddagger}\) & - & 74.50 & 97.10 & 88.10 \\ Statement \({}^{\ddagger}\) & - & 75.50 & - & 88.90 \\ GSAT \({}^{\ddagger}\) & 96.50 & 84.81 & 96.74 & 90.48 \\ COMER \({}^{\ddagger}\) & - & - & 97.10 & 88.60 \\ Full Bert \({}^{\ddagger}\) & - & 74.50 & 97.70 & 90.40 \\ TEN \({}^{\ddagger}\) & - & 77.30 & 97.10 & 90.80 \\ SUMBT \({}^{\ddagger}\) & - & - & 97.10 & 91.00 \\ Seq2 Seq-DU \({}^{\ddagger}\) & 85.00 & 74.50 & - & 91.2 \\ AG-DST \({}^{\ddagger}\) & - & - & - & 91.37 \\ BLA & **99.56** & **87.31** & **97.83** & **93.73** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Joint Goal Accuracy and Turn Request Accuracy on DSTC-2 and Woz-2.0. \({}^{\ddagger}\) is used for reproduced results from official codes. \({}^{\ddagger}\) is used for the reported results in respective papers.