Task-oriented dialog systems have been applied in various tasks, such as automated personal assistants, customer service providers and tutors. These systems work well when users have clear and explicit intentions that are well-aligned to the systems' capabilities. However, they fail if users intentions are not explicit. To address this shortcoming, we propose a framework to interleave non-task content (i.e. everyday social conversation) into task conversations. When the task content fails, the system can still keep the user engaged with the non-task content. We trained a policy using reinforcement learning algorithms to promote long-turn conversation coherence and consistency, so that the system can have smooth transitions between task and non-task content. To test the effectiveness of the proposed framework, we developed a movie promotion dialog system. Experiments with human users indicate that a system that interleaves social and task content achieves a better task success rate and is also rated as more engaging compared to a pure task-oriented system.
Current task-oriented dialog systems focus on completing a task together with the user. They can perform bus information search @cite_23 , flight booking @cite_1 , direction giving @cite_25 , etc. However, these systems can only focus on one task at a time. The famous personal assistants, such as Apple's Siri are composed of many of these single-task systems. These single-task systems' underlying mechanisms are mainly frame-based or agenda-based @cite_26 . The architecture of traditional dialog systems is slot-filling, which pre-defines the structure of a dialog state as a set of slots to be filled during the conversation. For an airline booking system, an example slot is destination city". An example corresponding system utterance generated from that slot is "Which city are you flying to?" Recently researchers have also started to look into end-to-end learning for task-oriented systems. Though the progress is still preliminary @cite_9 , the premise of having a learning method that generalizes across domains is appealing.
@cite_26 @cite_9 @cite_1 @cite_23 @cite_25
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Dialog management can be seen as a solution to two specific problems: (1) providing a coherent overall structure to interaction that extends beyond the single turn, (2) correctly manage mixed-initiative interaction, allowing users to guide interaction as per their (not necessarily explicitly shared) goals while allowing the system to guide interaction towards successful completion. We propose a dialog management architecture based on the following elements: handlers that manage interaction focussed on tightly coupled sets of information, a product that reflects mutually agreed-upon information and an agenda that orders the topics relevant to task completion. Traditional dialog systems used in goal-oriented applications require a lot of domain-specific handcrafting, which hinders scaling up to new domains. End-to-end dialog systems, in which all components are trained from the dialogs themselves, escape this limitation. But the encouraging success recently obtained in chit-chat dialog may not carry over to goal-oriented settings. This paper proposes a testbed to break down the strengths and shortcomings of end-to-end dialog systems in goal-oriented applications. Set in the context of restaurant reservation, our tasks require manipulating sentences and symbols, so as to properly conduct conversations, issue API calls and use the outputs of such calls. We show that an end-to-end dialog system based on Memory Networks can reach promising, yet imperfect, performance and learn to perform non-trivial operations. We confirm those results by comparing our system to a hand-crafted slot-filling baseline on data from the second Dialog State Tracking Challenge (, 2014a). We show similar result patterns on data extracted from an online concierge service. Abstract This paper describes PEGASUS, a spoken dialogue interface for on-line air travel planning that we have recently developed. PEGASUS leverages off our spoken language technology development in the ATIS domain, and enables users to book flights using the American Airlines EAASY SABRE system. The input query is transformed by the speech understanding system to a frame representation that captures its meaning. The tasks of the System Manager include transforming the semantic representation into an EAASY SABRE command, transmitting it to the application backend, formatting and interpreting the resulting information, and managing the dialogue. Preliminary evaluation results suggest that users can learn to make productive use of PEGASUS for travel planning, although much work remains to be done. In this paper, we describe how a research spoken dialog system was made available to the general public. The Let’s Go Public spoken dialog system provides bus schedule information to the Pittsburgh population during off-peak times. This paper describes the changes necessary to make the system usable for the general public and presents analysis of the calls and strategies we have used to ensure high performance. An attempt is made to ascertain rules for the sequencing of a limited part of natural conversation and to determine some properties and empirical consequences of the operation of those rules. Two formulations of conversational openings are suggested and the properties "nonterminality" and "conditional relevance" are developed to explicate the operation of one of them and to suggest some of its interactional consequences. Some discussion is offered of the fit between the sequencing structure and the tasks of conversational openings.