## 2 Related Work
### Deep-Learning-Based Approaches
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Model** & **Methodology** & **Strengths** & **Limitations** & **Performance (TG, TR, JGA)** \\ \hline GLAD & \(\bullet\) & Encodes each utterance and previous system actions by modeling slot-value pairs using BiLSTM and learns slot specific features. & \(\bullet\) & Estimates each slot-value pair independently. Learns feature representations via parameters sharing. & \(\bullet\) & Each slot type requires a separate model to train. Pretrained Glove embeddings and character n-gram for words representation. & TG, TR, JGA \\ \multirow{6}{*}{[8]} & \(\bullet\) & The attention mechanism computes the slot-specific temporal and context summary & \(\bullet\) & Generalization on rare slot-value pairs. & \(\bullet\) & Pretrained Glove embeddings and character n-gram for words representation. \\ \hline TEN [34] & \(\bullet\) & Captures temporal dependences across dialogue turns using GRU. The classifier extracts feature at each turn to predict the turn level state from current system action and user utterance. & \(\bullet\) & Improves turn level state prediction. Reduces the state aggregation errors. Requires less parameter estimation for the state aggregator. & \(\bullet\) & Needs to be improved with respect to graph factor with another graph neural network for state aggregation. \\ \multirow{6}{*}{[34]} & \(\bullet\) & The graph factor aggregates state dependencies. & \(\bullet\) & Robust for the state aggregation. & \(\bullet\) & Pretrained Glove embeddings and character n-gram for words representation. \\ \multirow{6}{*}{[34]} & \(\bullet\) & Belief propagation handles the uncertainties in state aggregation. & \(\bullet\) & Conceptually complex framework due to feature engineering. & \(\bullet\) & Bernoulli \\ \hline SeqSeq-DU [61] & \(\bullet\) & BERT-based encoder for the utterances in sequence-to-sequence fashion. & \(\bullet\) & Sequence-to-sequence framework to model intensity, slots and slot-values as global. & \(\bullet\) & Closely related to specific dialogue contexts. \\ \multirow{6}{*}{[56]} & \(\bullet\) & Schema descriptions deal with unseen domains. & \(\bullet\) & Learns representations of current and previous utterances during encoding and schema descriptions. & \(\bullet\) & Low prediction accuracy. \\ \multirow{6}{*}{[56]} & \(\bullet\) & Attention between utterance embeddings and schema descriptions. & \(\bullet\) & Scalable. & \(\bullet\) & \\ \hline MSP & \(\bullet\) & Slot specific memory holds slots and values to handle state update. & \(\bullet\) & Update dialogue state accurately. Previous wrong predicted values may correct in new value prediction & \(\bullet\) & The framework update state may reduce the performance of general tracking the slot-value pairs. \\ \multirow{6}{*}{[56]} & \(\bullet\) & BERT for dialogue contextual representation. & \(\bullet\) & Previous wrong predicted values may correct in new value prediction & \(\bullet\) & Increases the usage of system’s memory \\ \cline{2-5}  & \(\bullet\) & Hit type prediction layer maps representations of memory and dialogue contexts. & \(\bullet\) & Handles unseen values during training robustly. & \(\bullet\) & Requires intent detection module. \\ \multirow{6}{*}{[62]} & \(\bullet\) & Fills slots with values copied from the context, predictions from previous terms or system informs. & \(\bullet\) & Economically alternative to data augmentation to prevent memorization and over-fitting. & \(\bullet\) & Weak supervision and sparsity of data may render model prone to errors in the prediction of slot-value pairs. \\ \multirow{6}{*}{[62]} & \(\bullet\) & Feed forward network for classification. & \(\bullet\) & Deconically alternative prevent memorization and over-fitting. & \(\bullet\) & \(\bullet\) & \\ \multirow{6}{*}{[62]} & \(\bullet\) & Triple copy strategy to copy values from the context, predictions, previous turns and system information. & \(\bullet\) & Domain agnostic to facilitate transfer to unseen slots and d & \(\bullet\) & \(\bullet\) & \\ \multirow{6}{*}{[62]} & \(\bullet\) & Slot attention layer to token representations of dialogue turn. & \(\bullet\) & Capable to track new domains by learning from non-dialogue data. & \(\bullet\) & \(\bullet\) & \\ \hline \hline \end{tabular}
\end{table}