## 2 Related Works  
[PERSON] et al. [14] have presented a semantic-enhanced Gaussian mixture method (SEG) for unknown intent identification. In particular, the authors devised utterance embeddings that have a Gaussian mixture distribution, and they infused dynamic-class semantic data into Gaussian means that allow for the learning of more class-concentrated embeddings, which can be helpful in facilitating downstream outlier recognition. Coupled with a density-related, outlier-identification method, SEG has attained competitive outcomes on three real, task-based, dialogue datasets in two languages for unknown intent recognition. [PERSON] and [PERSON] [15] have examined an attention-related neural network (NN) method which executes multi-label categorization to identify many intents and generates labels for intents, as well as slot-labels, at the token-level. The authors displayed the existing performance for slot-label identification and intent detection through a comparison with strong, newly formulated methods.  
[PERSON] et al. [16] have presented ProtAugment, a meta-learning technique for short text classification (an intent-detection task). ProtAugment is a new extension of the Prototypical Network, which confines overfitting to the bias presented by a few-shots classifier objective at every episode. It depends on various paraphrasing. It is a conditional language method that has become the initial, fine-tuned language for paraphrasing, and diversity was later presented at the decoding phase of every meta-learning episode. [PERSON] et al. [17] have proposed a method for forecasting slots and the intent of queries for chatbots that will answer career-based queries. The authors considered a multistage technique in which both the process (slot-tagging and intent-classification) imitate one another's decision-making at various levels. The method segregates the issue into stages, figuring out single issues, and transferring the related outcomes of the present stage to the following stage, thus minimizing the search space for consequent levels, and finally, it performs categorizations and tagging that are more viable after every stage.  
In [18], the authors began from the nature of out-of-domain (OOD) intent classifications and predicted its optimizing objective. The authors even presented an effective technique, called k-nearest-neighbor (KNN) contrastive learning. This technique uses KNN for learning discriminatory semantic features that are highly conducive to OOD identification. [19] enhanced intent-detecting performance by using datasets that were gathered in the same field, but which have various intent groups. In the baseline mechanism, the authors used the existing, pre-trained transforming techniques with an intent-identification-classifier layer. This technique contains two fine-tuned stages. In the initial stage, the variables of the classifier and transformer layers were adapted utilizing an in-domain dataset to the targeted set. Then, the author retained the transformer parameters, which were updated, but replaced the classifier with a newer one designed for the targeted task. In the second stage, the network was finely tuned endwise, utilizing the targeted set.  
[PERSON] et al. [20] modeled an innovative domain-regularized module (DRM) for reducing the overconfident phenomenon of a vanilla classifier, attaining superior generalization in both cases. In addition, DRM was utilized as a drop-in replacement for the final layer in any NN-related intent classifier, offering a low-cost method for an important enhancement. [PERSON] et al. [21] introduced an Adaptive Graph-Interactive Framework (AGIF) for joint, multiple-intent recognition and slot-filling, where the authors introduced an intent-slot graph communication layer for modeling strong relations among the intents and slots. Such communication layers can be implemented for every token adaptively, which has the benefit of mechanically deriving the related intent data, forming a finely grained intent data compilation for token-level slot estimation.