## 2 Related Work
### Traditional DST Approaches  
Most of the DST detection methods in the literature employ rule-based and machine-learning approaches. A spoken dialogue system (SDS) can be imitated as a decision process where the uncertainty in tracking its dialogue state occurs due to errors that pervade through ASR and SLU/NLU [40; 41; 42]. These impairments were resolved by [43] through a simple and generic rule-based DST mechanism called GENR. It is based on simple domain-independent rules that use basic probability operations without requiring external knowledge. It infers the immediate information gained from observable system actions and partially observed user acts. The observation is SLU n-best list and normalized confidence scores, which are similar to those proposed in [3; 44; 45]. However, GENR targets miss the intermediate analysis of the information gained from SLU n-best lists and the confidence scores are produced without learning prior knowledge. Additionally, GENR uses data-driven methods or is designed based on domain-specific strategies beyond considering only the SLU hypothesis. Another popular approach to predict labels, annotations, or slot-value pairs in DST is the use of maximum margin classifiers, such as support vector machines (SVMs). For instance, [36] proposed a hybrid-based architecture consisting of traditional and deep-learning-based approaches (see next section for DL-based approaches of the model). They used a semantic parser that requires a set of SVMs trained on n-gram features to predict the dialog act type by multi-class SVM, and a binary SVM to predict the existence of each slot-value pair from an utterance. The dialogue context features and the n-gram features of the original STC parser were exploited to constrain the semantic parser. The final vector is obtained by combining the original n-gram feature with the reciprocal of the turn index and the context features from the last system action. Finally, normalization via a semantic parser is applied to each of the top ASR hypotheses to guarantee the sum for generating confidence scores to test and predict the dialogue act. Few other approaches [46; 47; 48] assume that dialogue can be modeled as a probabilistic model such as Bayesian network that relates dialogue state \(s\) to the system action \(a\), the true or unobserved user action \(u\), and ASR or SLU result \(\hat{u}\) as cited in Figure 3. When the ASR/SLU and the system action are observed, Bayesian inference is applied to compute the distribution over possible dialogue states. Several probabilistic formulations have been researched for relating these quantities [46; 47]. For example, [48] employed a dynamic Bayesian network to learn the user action for a given text directly from the machine-transcribed corpus. In another work, [PERSON] et al. [49] used constrained Markov Bayesian polynomial (CMBP) based on Bayes theorem to enhance the power of the rule-based model. The primary idea was to demolish the necessary probabilistic conditions and prior knowledge to overcome the optimization problem for the DST model during training. Some other opportunities such as maximum entropy (MaxEnt) used in belief tracking mechanism to calculate beliefs [50] upon a state through parametric models, which directly present the belief \(b(s_{t+1})=p(s_{t+1}|s_{t},z_{t})\) in connection to maximum entropy that has been widely implemented as discriminative approach [51] in variety of models. It creates the belief methodically as \(p(s|x)=\eta.e^{\eta^{x}\varepsilon(x,s)}\); where \(\eta\) represents normalizing constant, \(x=(d_{1}^{u}),d_{1}^{m}),s_{1},\ldots,d_{1}^{u}),d_{m}^{m}),s_{t})\) denotes the history of user dialogue acts, \(d_{i}^{u}\varepsilon\{1,\ldots t\}\) are system dialog acts, \(d_{i}^{m}\varepsilon\{1,\ldots t\}\) presents the sequence of beliefs into the current dialogue at time \(t,\phi(. )\) is vector features on \(x\) and \(s\), and \(w\) denotes the set of model parameters learned from the annotated dataset. [PERSON] et al.