## 2 Related Work
### Deep-Learning-Based Approaches  
In recent years, numerous studies applied DL-based approaches (DLA) to natural language processing (NLP) problems [52; 53] including DST in spoken dialogue systems [21; 29]. DLA is a set of algorithms, i.e., deep neural networks (DNNs) such as convolutional neural network (CNN), gated recurrent unit (GRU), long short-term memory (LSTM), attention mechanism, and BERT, that can learn a complex mapping between input and output space [54]. These approaches demonstrated excellent performance on DST datasets such as WoZ-2.0 [17] and DSTC-2 [18]. Moreover, DLA has produced promising results in the field of DST for learning semantic neural representations of words [55; 56], point slot values in conversation [24], neural dialogue generation [57], and sharing parameters among estimators for various dialogue states [16; 58] as well outlined in Table 1.  
In connection with the above discussion, [PERSON] [59] proposed a deep neural network as a classifier for the DST mechanism to train the entry of the DST challenge dataset. The aim of deploying the DNN was for improving the probability distribution over possible values by learning the tied weights and use of sliding windows. [PERSON] et al. [36] designed four DNNs for joint goals (one for each informable slot), one for method, and one for requested slots. These DNNs have a similar structure with softmax for output layer activation and sigmoid for hidden layer activation. Each DNN takes the feature set of a certain value of the goal, method, or requested slots as the input and outputs two values through the hidden layer to predict the confidence score as output. [PERSON] et al. [12] presented a neural belief tracker (NBT) that employed multi-layers of neural networks including CNN. It uses the previous system output, viz., the last utterance takes the user utterance as the current utterance and parses the system output and current utterance to come up with the current state of dialogue. Another framework, GLAD [8] designed LSTM with the combination of attention mechanism to estimate each slot value pair independently. It uses global modules to share parameters between estimators for each slot and local modules to learn slot-specific feature representations. [PERSON] et al. proposed a multi-attention-based scalable DST (MA-DST) framework [60] to tackle DST as a classification problem. The model contains GRU, and multi-attention to encode the dialogue context and slot semantics that enhance the performance of DST robustly in a scalable manner and perceive the unseen words during training. Several studies rely on hand-crafted rules to extract the features and delexicalization for lexical errors [31; 43; 63] that rise the scalability challenge in the framework during the classification task for DST. It has been noticed that stacked BiLSTM [38] is a better alternative for features extraction [64; 65]. Stacked BiLSTM is the extension of LSTM and a deep bidirectional neural network that learns deeper representations up to the two or more layers [38]. It performs better on classification tasks for speech recognition and prediction-based management systems such as domain and intent detection of dialogue systems [38; 64].