A ir C oncierge: Generating Task-Oriented Dialogue via Efficient Large-Scale Knowledge Retrieval (2020)

Abstract: Despite recent success in neural task-oriented dialogue systems, developing such a real-world system involves accessing large-scale knowledge bases (KBs), which cannot be simply encoded by neural approaches, such as memory network mechanisms. To alleviate the above problem, we propose , an end-to-end trainable text-to-SQL guided framework to learn a neural agent that interacts with KBs using the generated SQL queries. Specifically, the neural agent first learns to ask and confirm the customer’s intent during the multi-turn interactions, then dynamically determining when to ground the user constraints into executable SQL queries so as to fetch relevant information from KBs. With the help of our method, the agent can use less but more accurate fetched results to generate useful responses efficiently, instead of incorporating the entire KBs. We evaluate the proposed method on the AirDialogue dataset, a large corpus released by Google, containing the conversations of customers booking flight tickets from the agent. The experimental results show that significantly improves over previous work in terms of accuracy and the BLEU score, which demonstrates not only the ability to achieve the given task but also the good quality of the generated dialogues.

## introduction the task-oriented dialogue system @xcite is one of the rapidly growing fields with many practical applications, attracting more and more research attention recently @xcite @xcite @xcite . in order to assist users in solving a specific task while holding conversations with human, the agent needs to understand the intentions of a user during the conversation and fulfills the request. such a process often involves interacting with external kbs to access task-related information. figure 1 shows an example of a taskoriented dialogue between a user and an airline ticket reservation agent. traditional dialogue systems @xcite may rely on the predefined slot-filling pairs, where a set of slots needs to be filled during the conversation. in addition, some works @xcite @xcite have considered integrating kbs in a task-oriented dialogue system to generate a suitable response and have achieved promising performance. however, these methods either are limited by predefined configurations or do not scale to large kbs. since real-world kbs typically contain millions of records, end-to-end dialogue systems are not able to incorporate external kbs effectively, leading to unstable dialogue responses. moreover, very few research has attempted to explore how to efficiently cooperate with kbs or taken resource consumption, such as flops or memory space, into consideration when designing the model. in order to solve the issues mentioned above, we propose airconcierge, an sql-guided task-oriented dialogue system that can efficiently work with real-world, large-scale kbs, by formulating sql queries based on the context of the dialogue so as to retrieve relevant information from kbs. we evaluate and demonstrate airconcierge on airdialogue @xcite , a large-scale airline reserving dataset published recently. airdialogue has high complexity in contexts, creating the opportunity and the necessity of forming diverse taskoriented conversations. our experiments show that airconcierge achieves improvements in accuracy and resource usage compared to previous work. ## the proposed framework our design of the airconcierge system addresses the following challenges in developing an effective task-oriented dialogue system, including ## conclusions we propose airconcierge, a task-oriented dialogue system that has high accuracy in achieving the user's tasks. by employing a subsystem, including a dialogue state tracker and a sql generator, airconcierge can issue a precise sql query at the right time during a dialogue and retrieve relevant data from kbs. as a result, airconcierge can handle large-scale kbs efficiently, in terms of shorter processing time and less memory consumption. using a precise sql query also filters out noise and irrelevant data from the kbs, which improves the quality of the dialogue responses. our experiments demonstrate the better performance and efficiency of airconcierge, over the previous work. the agent responds no matching flight along with a reason. for instance, the agent in the upper sample mentions that no matching flights found is due to the mismatching dates. for "no reservation",