End-to-End Task-Oriented Dialogue Systems Based on Schema (2023)

Abstract: This paper presents a schema-aware end-to-end neural network model for handling task-oriented dialogues based on a dynamic set of slots within a schema. Contrary to existing studies that proposed end-to-end approaches for task-oriented dialogue systems by relying on a unified schema across domains, we design our approach to support a domain covering multiple services where diverse schemas are available. To enable better generalizability among services and domains with different schemas, we supply the schema’s context information including slot descriptions and value constraints to the model. The experimental results on a well-known Schema-Guided Dialogue (SGD) dataset demonstrated the performance improvement by the proposed model compared to state-of-the-art baselines in terms of end-to-end modeling, dialogue state tracking task, and generalization on new services and domains using a limited number of dialogues.

## introduction task-oriented dialogue (tod) systems enable a flexible alternative for users to fulfill tasks, such as flight booking, arranging transportation, and checking the weather, through a natural language conversation with a virtual assistant. due to the potential benefits of such a system for both users and industries, an increasing number of application domains and individual services within the domain are available for the system to support. typically, each service can be distinguished by its schema which determines different functionalities and criteria for the task. to maintain user engagement and satisfaction, it is important for the system to be adjustable to a variety of schemas to effectively interpret the user's goal as well as deliver appropriate responses. to promote the system's scalability and transfer ability, recent studies proposed approaches that uti1 the code is available at https://github.com/ aistairc/schematod . restaurants_1 … slot: description: constraints: type: restaurant_name name of the restaurant non-categorical slot: description: constraints: type: date date for the reservation or to find availability non-categorical slot: description: constraints: type: cuisine cuisine of food served in the restaurants mexican, chinese, indian, american, italian categorical slot: description: constraints: type: serves_alcohol boolean flag indicating if the restaurant serves alcohol true, false categorical restaurants_2 … slot: description: constraints: type: restaurant_name name of the restaurant non-categorical slot: description: constraints: type: date tentative date of restaurant reservation non-categorical slot: description: constraints: type: category the category of food offered by the restaurant non-categorical slot: description: constraints: type: has_seating_outdoors whether the restaurant has outdoor seating available true, false categorical lize a pre-trained language model @xcite @xcite such as @xcite and t5 @xcite . these approaches identify task-specific slots and values within a user input utterance, track belief states (i.e. user's goals), retrieve database records using current belief states, estimate an appropriate system action, and translate the system action to a natural language response in an end-to-end manner. these approaches allow the system to be generalized to a new schema of a new domain, however, they are based on the assumption that each domain is associated with a unified schema where its schema elements (i.e. slots) are commonly shared across domains. for example, address and postcode are used as shared slots for both hotels and restaurants domains. this assumption does not necessarily reflect a real-world scenario where multiple services with different schemas exist for a single domain. each service schema can consist of slots that are semantically similar to slots of other services, and slots that are specific to its own service. as illustrated in figure 1 without addressing the differences in schemas of multiple services of the same domain, the system would unsuccessfully associate values within the user utterance with relevant slots that are applicable for each service. this could lead to inaccurate tracking of the user's goal which in turn, causes the system to generate an irrelevant response. therefore, in this work, we proposed a schema-aware end-to-end neural network model, initialized with a pre-trained language model t5 @xcite , to handle task-oriented dialogues of multiple services and domains with diverse schemas. our proposed model is adaptable to a dynamic set of slots within a schema as we feed slot descriptions and value constraints as the additional context information, along with the dialogue history to the model. for the experiments, the variations of the proposed model using different schema information were evaluated and compared to recent approaches for end-to-end tod systems using a publicly available dataset, schema-guided dialogue (sgd) dataset @xcite . according to the experimental results, the variations of the proposed model outperformed the baselines in all evaluation metrics in terms of the end-to-end modeling and dialogue state tracking (dst) task. in addition, our proposed model demonstrated improved generalizability on new services and domains under zero-shot and few-shot settings. ## conclusion in this paper, we focus on improving the end-to-end tod systems to support a domain covering multiple services as well as to achieve better generalizability. we propose schematod, a schema-aware end-to-end neural network model, which incorporates context information of slots within a service schema of a domain. experiments conducted on the sgd dataset suggest that our proposed model when using both slot descriptions and value constraints achieved the best performance when the domains consist of only unseen services and when the domains are unseen with more categorical slots. otherwise, the proposed model using only slot descriptions outperformed other models for the domains with seen services and the unseen domains with a high number of non-categorical slots. for generalizability, the performance achieved by the proposed model demonstrated improvement over the baselines when using a few dialogue examples of the new services and domains.