# STOD: Towards Scalable Task-Oriented Dialogue System on MultiWOZ-API
## 1 Introduction  
Task-oriented dialogue (TOD) systems are playing an increasingly important role in our daily lives, such as personal assistants and customer service. With the increasing number of dialogue tasks, the scalability of TOD models is becoming a key factor hindering the widespread adoption of TOD systems.  
Existing work can be mainly classified into two categories. The one is focused on the scalability of a single module in the pipeline system, but it is difficult to compose an effective scalable TOD system by combining multiple scalable modules due to error propagation. The other is end-to-end TOD system modeling. Compared with the pipeline system, the end-to-end system generates dialogue states, dialogue actions, and dialogue responses in a cascaded order, which is easier to scale. However, the existing end-to-end dialogue systems have strong scalability in slots and dialogue actions but show weak scalability in dialogue policy, which is more important and caused by the following reasons.  
**Firstly, the dialogue state consists of a set of domain-specific slots and their values.** When scaling to a new domain, the existing models can predict the correct values of the similar slots with the source domains, but the other dialogue states are difficult to predict correctly. **Secondly, the existing models simplify the DB results** (only input the number of entities that meet the user's requirements). Incomplete DB results let the model learn a database-specific dialogue policy, which is biased when scaled to new domains. **Finally, the existing models delexielize the response for learning value-independent parameters [1]**, making it difficult to learn the grounding ability based on DB results in the dialogue policy.  
To address the aforementioned problems, we propose a new scalable TOD modeling framework shown in Figure 1. **Firstly, we predict the API query which can be directlyapplied to interact with the database instead of the dialogue state.** Different from the domain-specific dialogue state, the API query is API-specific, and different domains have similar API, which is more scalable between different domains. **Secondly, we use the complete DB results to learn the dialogue policy** so that the dialogue policy learning is decoupled from the specific database. **Finally, we predict the complete response based on the API query and the complete DB results** so that the model can learn the grounding capabilities in the dialogue policy.  
In order to verify the effectiveness of our proposed framework, firstly we annotated the API query on the multi-domain TOD dataset MultiWOZ2.1 [2]. We interacted with the database according to the annotated API query and obtained the complete DB results. Secondly, we built a multi-domain scalable end-to-end TOD system, which combines natural language utterances and structured API queries into a unified text-to-text manner. Finally, in order to verify the scalability of our model in cross-domain settings, we built a cross-domain end-to-end TOD system.  
Our main contributions are as follows.  
First, we propose a Scalable Task-Oriented Dialogue modeling framework (STOD). Compared to existing work, it is more friendly to improve the scalability of TOD systems.  
Second, we build a new API-schema-based MultiWOZ-API by re-annotating MultiWOZ2.1 [2] to evaluate the scalability of the task-oriented dialogue system.  
Third, based on STOD, we propose a scalable multi-domain task-oriented dialogue model (MSTOD) and a cross-domain task-oriented dialogue model (CSTOD). The experimental results on MultiWOZ 3.0 show that MSTOD and CSTOD outperform the SOTA baseline models in multi-domain and cross-domain settings, respectively.