## 2 Related Work
### Deep-Learning-Based Approaches

Table 1: Comparison of latest studies exploring the DST problem, where TG, TR, and JGA, denote Turn Goal, Turn Request, and Joint Goal Accuracy, respectively. In another study, [66] introduced a model to deal with DST as a sequence-to-sequence problem and mitigate other problems including scalability and unseen schema in new domains on multi-domain datasets. The model employed BERT to learn and utilize a better representation of the current and previous utterances. Moreover, a pointer network was utilized to build the representations of intents, slots, and values among their corresponding values. [PERSON] et al. [66] employed to take the previous dialogue state and the dialogue of the current turn to produce a primitive dialogue state. Later, the current turn of the dialogue and primitive dialogue state is passed to the amendable generator to generate the amendable dialogue state that contains the mistake-free dialogue state for further predictions of dialogue states. [PERSON] et al. [28] developed a stack-propagation framework to jointly process the slot filling and come up with end-to-end DST by exploiting a BERT-based dialogue history encoder to procure the dialogue history representation and initiate the slot value decoder. They employed a slot mask attention mechanism to get key slot information detected by slot filling to improve the updated dialogue state. They used a slot-value softcopy mechanism to exploit words marked by slot filling. [PERSON] et al. [58] used two BERT-based encoders and designed a hybrid approach for fixed-ontology- and open-vocabulary-based DST. They defined pick list-based slots for classification and span-based slots for span extraction as DST readers. [PERSON] et al. [67] employed a contextual hierarchical attention network based on BERT and an adaptive objective to alleviate the slot imbalance problem by dynamically adjusting the weights of slots during training. To overcome the substantial noise in the state annotation, [PERSON] et al. [38] used a pre-trained language model BERT such as [11; 15; 55] that was fine-tuned during training and encoded dialogue context into contextual semantic representations. [PERSON], [PERSON], and [PERSON] [68] worked on the noise labels in the state annotations of datasets to increase the efficiency of DST. They employed one BERT to encode the dialogue context into contextual semantic representations and another one to encode the candidate slot-value pairs into semantic vectors. Multi-head slot attention was used to obtain the information relevant to all slots from the same dialogue context. [PERSON] et al. [69] developed IC-DST, a text-to-SQL approach to define each domain as a table and each slot as a column for dialogue states representations. The aim was to overcome the limitations of fine-tuning pre-trained language models, retrain the systems again when a new slot or domain is added, and achieve reasonable performance on few-shot settings. In summary, by analyzing the related works presented in this section, we suspect that DST needs to focus on managing scalability problems, unseen values observed during training, and retrain the model when new values appear. This conclusion formalizes DST as a sentence classification task, that can be processed by generating slots and their corresponding values from the learning through the current turn and dialogue history to recognize the dialogue state in fixed domain-ontology-based frameworks. Therefore, we devised a model that is capable of sharing parameters across the slot types and scalable on both datasets for addressing the scalability issue. The model takes candidate slots and value pairs as input to preserve them in the sequence of words. This way, the model can be applied directly to the newly occurred slot values that were unseen during training. The Stacked BiLSTM should be used to exploit features from the last hidden state of BERT to capture the contextual and semantic features of words. This will improve the classification of slot-value pairs from user and system utterances.