# Leveraging Dialogue State Tracking for Zero-Shot Chat-Based Social Engineering Attack Recognition
## 3 Background
### Dialogue-State Tracking (DST)  
Dialogue-state tracking [31] is the process of maintaining an accurate representation of the current state of a conversation. This involves identifying the user's intentions and goals, as well as the entities and concepts that are mentioned in the conversation, in order to provide relevant and precise responses. To tackle the challenge of representing the dialogue state, [PERSON] et al. [32] proposed a method that leveraged dialogue acts. They employed a partially observable Markov decision process (POMDP) to build systems that could handle uncertainty, such as dialogue systems. To achieve a practical and feasible implementation of a POMDP-based dialogue system, the state can be factorized into discrete components that can be effectively represented by probability distributions over each factor [33]. These factorized distributions make it more feasible to represent the most common slot-filling applications of POMDP-based systems, where the complete dialogue state is reduced to the state of a small number of slots that require filling. In 2020, [PERSON] et al. [4] proposed the schema-guided paradigm to tackle dialogue-state tracking, which involved predicting a dynamic set of intents and slots by using their natural language descriptions as input. They also introduced a schema-guided dataset to evaluate the dialogue-state tracking tasks of dialogue systems, including domain prediction, intent prediction, and slot filling. Additionally, they developed a DST model capable of zero-shot generalization. The schema-guided design utilizes modern language models, such as BERT, to create a unified dialogue model for all APIs and services by inputting a service's schema, which enables the model to predict the dynamic set of intents and slots within the schema. This approach has gained significant attention, and annual competitions, such as the Dialogue Systems Technology Challenge (DSTC), track the progress in this field [34]. To achieve slot filling, a sequence model can be trained using different types of dialogue acts as classification labels for each individual domain. Additionally, pretrained language models, such as GPT [35], ELMo [36], BERT [37], and XLNet [38], have shown significant promise in recent years with regards to natural language processing. These models have outperformed prior algorithms in terms of generalization and zero-shot learning. Consequently, they offer an effective approach to performing zero-shot learning for language understanding. Furthermore, by leveraging pretrained language models in the schema-guided paradigm, dialogue systems can generalize to unseen service schema elements and improve their accuracy and robustness. The primary objectives of dialogue-state tracking (DST) encompass predicting the active user intention, requested slots, and values of slots in a given conversation turn. Within the context of DST, the user intention closely relates to the service supplied by the dialogue system. The intention refers to the user input's purpose or goal, while the service represents the functionality offered by the dialogue system to achieve the user's intent. The user's intention is typically deduced from the input and ongoing conversation state and can be expressed as a label or a set of labels that indicate the user's objective. A service denotes the task or action the dialogue system intends to perform and can be defined by a group of intentions and slots. The slots indicate the requested or supplied information. For example, an intention might involve \"booking a flight,\" while the slots could consist of \"destination,\" \"departure date,\" \"return date,\" and other related information. Services may vary over time, making it critical to have a flexible and adaptable ontology that can be modified as required. DST's capacity to handle either a closed set of slots (static ontology) or an open set of slots (dynamic ontology) is a crucial attribute.