### Research Background  
Recently, DST has gained popularity in both industry and academia, as it is capable of determining the users' intentional states at each turn of the dialogue [9; 10]. A state holds a set of dialogue acts and pairs of predefined slots and their corresponding values [11; 12] in the form of dialogue states. Figure 2 presents an example of a dialogue conversation regarding a restaurant reservation taken from the WoZ-2.0 dataset. Here, we can see the dialogue acts (inform or request) and the slot value pairs (price range = expensive, food = Australian, food = Mediterranean). The dialogue state of the conversation is estimated by DST over the conversation history and current utterance of the user [8]. A state's accurate prediction is significant as it enables the system to perform the next action with greater accuracy and efficiency, and produces a personalized response for the target user. For accurate state prediction, the DST models are either based on fixed or open vocabulary [13]. The fixed vocabulary-based DST models define a collection of slots and values from a predefined ontology [9; 13], to extract features or identify patterns and build relations among the entities. The ontologies contain slots with known predefined values [14; 15; 16]. Moreover, these methods have demonstrated highly competitive performance over some popular DST datasets, such as DSTC-2 and WoZ-2.0 [17]. However, they have conceptually complex architectures and are heavily engineered [18; 19; 20]. Additionally, several models operate on fixed domain ontology and each slot type requires a separate model to train in the corresponding domain [8; 12; 20], which consequently increases complexity. Thus, the number of parameters and slot types are proportional to each other, rendering these approaches  
Figure 1: The principal components of a typical conversational system. Figure 2: A toy example of dialogue conversation based on WoZ-2.0 dataset. prone to scalability issues. Although they can predict the values that are not available in history records, defining the possibly large slot value list per domain is quite challenging during the training phase. Formally, they are treated as a classification problem [12; 21]. On the other hand, open-vocabulary-based models generate or extract the values for each slot from the context simply by using the encoder-decoder architecture [22; 23] or pointer networks [24; 25; 26]. These approaches can share the parameters among the slots and domains in multi-domain datasets to overcome scalability issues [27]. They are flexible while extracting the slot values from dialogue history to solve the unseen problems [28]. However, various architectures are inefficient in context encoding from dialogue context because they encode all the previous utterances from history at each dialogue turn to aggregate the information [24; 26]. Additionally, they are limited in predicting the slot values that were not detected during training. To summarize, both DST approaches can handle scalability problems, parameters sharing over slots, and handling the growth of parameters when the domain ontology dynamically changes. However, the fixed-vocabulary-based DST tracker performs better on DSTC-2 and WoZ-2.0 datasets versus the multi-domain datasets [21; 29]. They can observe unseen slot values. Therefore, this study focuses on creating a vocabulary-based model and formalizing it as a classification problem. The notion behind considering this as a classification problem is to further improve the performance of DST [19; 30; 31; 32; 33]. These models use recurrent networks to extract features from the history of the system and the user's sentence pairs. A classifier is employed upon the extracted features to predict the dialogue states.