## 4 Experiments
### Baseline Models  
This section discusses and compares the following baseline models to assess the performance of our proposed model. * **NBT** Neural Belief Tracker (NBT) [20] updates its internal representation of the states of conversion at each turn in data driver fashion. It was the first neural-based approach for DST, and used word embedding performing on par with the models developing engineered lexical rules. * **GLAD** Global Locally Self Attentive Dialogue State Tracker (GLAD) [8] consists of two modules. The global module shares parameters among slots through self-attentive RNNs. The local module learns features that consider specific slots. The model uses the previous actions of the system and the current utterance of the user as input to compute meaningful similarities with predefined ontological terms. * **Statenet** Universal dialogue state tracker (Statenet) [16] generates a fixed length representation of the dialogue history and compares the distance between the representation and the value vectors in the candidate sets. It uses the original ASR information of the user utterances, information about the machine's acts, and literal names of slots and their corresponding values. The values from candidate sets are set to be dynamically changed in the model. * **GSAT** Global encoder and Slot-Attentive decoders (GSAT) [82] is a highly robust dialogue state tracker that predicts the dialogue states with or without using pre-trained embeddings. In addition, GSAT consists of a recurrent neural network-based single global encoder and slot-attentive decoders as classifiers. The encoder module encodes the current user utterance and previous system action from history to output the context vector and hidden representation for each token. The classifier module processes the hidden representations of the encoder and possible slot values to provide the probability distribution for each possible slot value. * **COMER** Conditional Memory Relation Network (COMER) [25] uses two sequential decoders to formulate dialogue state as a sequence generation problem instead of a pair-wise prediction problem. First, its encoder-decoder network generates the slot sequences in the dialogue state and then for each slot generates the corresponding value sequences by conditioning on the dialogue history. The parameters are shared across all the decoders to overcome the scalability of the hierarchical structure of the dialogue state. * **Full Bert** is a simple but effective BERT-based model [21] proposed for recourse-limited systems to track the dialogue states. The full BERT approach uses BERT to control the parameters to not grow when the ontology changes. It takes candidate and dialogue contexts and produces a score that indicates the relevance of the candidate by considering a dialogue context and a candidate slot-value pair. * **TEN** Neural dialogue state tracking with temporally expressive networks (TEN) [34] maintains the state of the system for tracking the progress of dialogue. In this approach, two aspects of state tracking were iterated to improve results. These include (a) temporal feature dependencies in model design and (b) uncertainties in state aggregation more expressively modeled. * **SUMBT** Slot-utterance matching for universal and scalable belief tracking [55] focuses on developing a scalable and universal DST. It uses an encoder to encode the system and user utterances. It provides the contextualized semantic representation of sentences. The encoder also encodes the slots and their corresponding values. It also learns the slot value relationships that appear in dialogues. * **Seq2 Seq-DU** A sequence-to-sequence approach to dialogue state tracking (Seq2 Seq-DU) [61] transforms all the utterances in dialogue into semantic frames. It employs encoding of utterances and schema descriptions sequentially to generate pointers in decoding of dialogue states. * **AG-DST** Amendable generation for DST [66] takes the dialogue of the current and previous turns as input.