## 5 Results and Discussion
### Comparative Analysis of Experimental Results  
The experimental results in Table 4 show the performance of proposed model in this study against the selected baselines on DSTC-2.0 and WoZ-2 datasets. It can be observed that the proposed model outperforms the baseline models on DSTC-2 in terms of joint goal and request accuracies. NBT performed poorer due to its use of dense embeddings and mapping directly from turns to states. Furthermore, it suffers from scalability problems to large output unbound space and requires a separate model to train for each slot type in the domain ontology. These limitations led to the model not sharing parameters across slots. Additionally, the slots are trained independently without sharing information among them. The second best results in Table 4 on the DSTC-2 dataset are produced by Seq2 Seq-DU, bearing a sequence-to-sequence scalable architecture that can share the parameters across all slots and domains for single and multi-domain datasets. It uses contextual embeddings that produce better results in DST. However, it is unable to focus more on the feature extraction over the BERT output and attention over all the non-zero words of input IDs from pre-trained contextual embeddings. However, the proposed model performed 2.2% better than Seq2 Seq-DU in terms of joint goal accuracy. This is because our model is more prominent in the feature extraction from the dialogue context through Stacked BiLSTM. Additionally, it considers the significance of relations among the values to their corresponding slots. The results produced on WoZ-2.0 in Table 4 are better than the DSTC-2 dataset. It is because the WoZ-2.0 dataset has less ambiguity with additional annotation corrections. The second-best results among the all other baseline models are produced by AG-DST in terms of joint goal accuracy. The reason behind the great impact on the results by AG-DST is that it utilizes the input sequence to compose the current turn dialogue from the previous dialogue state. Our proposed framework is scalable to share the parameters and create relations between values and their corresponding values. In contrast, NBT and GLAD produced poor results as compared to other models in respect of turn request and joint goal accuracies. NBT and GLAD have some limitations as compared to other latest frameworks in the way that the number of parameters is proportional to the number of slot types. Furthermore, they operate on fixed domain ontology and the neural architecture is heavily engineered and conceptually complex. However, our model produced high turn request and joint goal accuracies in comparison to the aforementioned baselines by 0.13% and 1.67%, respectively. Our model performed well because it can learn better contextual and semantic features as well as slot types, and possible values defined in advance and do not change dynamically. Furthermore, we convert the negative examples into positive examples to balance the datasets by random oversampling that decreases the errors. Additionally, the model does not require feature engineering such as lexicon and delexicalization due to the deployed Stacked BiLSTM and multiple attention mechanism. Another strength of the model is that it does not depend upon ontology size increase, given that the vocabulary does not change. Thus, the model is robust in terms of less computation as a consequence of the BiLSTM and multiple attention instead of complex architecture with feature engineering.