## 6 Conclusions and Future Work  
In this study, we have proposed a new framework BLA to find the accurate states for dialogue state tracking. The model is composed of BERT, Stacked BiLSTM, and multiple attention mechanisms to encode the dialogue context and candidate for feature extraction from utterances of the user and system to predict the dialogue state. Additionally, BLA allows parameters to share across the slots and slot-specific feature learning. BLA improves model performance by minimizing the mistakes from examples loaded from datasets after the conversion of negative examples into positive examples. To evaluate the performance of the model, we conducted experiments on two publicly available datasets WoZ-2.0 and DSTC-2, and analyzed the experiments with variants of the proposed model and baseline model. The result shows that BLA achieves state-of-the-art results on both datasets. The main findings and observations of the study include the following:  
* The proposed model, upon evaluation on two datasets, revealed that it extracts features more effectively due to the use of Stacked BiLSTM as compared to other DL-based and traditional approaches.
* We experimented with variants of the attention mechanism. The weaker performance of the model on the self-attention layer suggests that the multi-attention mechanism is helpful for understanding the features and phrases for dialogue state tracking.
* During hyper-parameter selection, we noticed that the learning rate was the most important parameter for the proposed model because the model regulates the number of allocated errors for updating weights to perfectly calibrate the accuracies.  
Besides these findings, the proposed framework bears some limitations. These include the need to be more flexible, scalable, and simple in order to work faster on multi-domain datasets. The parameter sharing can be further developed to render it more efficient for multi-domain datasets. Thus, we intend to incorporate a graph attention network to build the relationship between slots and their values that can enhance the performance of BLA. The decrement of parameters with the usage of BERT variants such as RoBERTa, DistilBERT, and ELECTRA may increase the robustness in DST, which is also under consideration. We plan to extend our model and conduct experiments on publicly available multi-domain datasets in the near future.  
Figure 7: Impact of Dropout Rate.  
**Author Contributions:** [PERSON] contributed to conceptualization, methodology, software, data curation, writing original draft. [PERSON] was involved in resources and analysis with constructive discussions. [PERSON] supervised and conceptualized the study. [PERSON] contributed to software and methodology. [PERSON] was involved in analysis and manuscript preparation. [PERSON] helped with conceptualization, methodology, writing--review and editing. [PERSON] contributed to formal analysis, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The research presented in this paper was partially supported by the Natural Science Foundation Program of China under grant number U21A20488.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets used in this research can be found in [34]. Readers may request the code of the proposed model from the corresponding authors.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.