This work presents an agenda-based approach to improve the robustness of the dialog manager by using dialog examples and n-best recognition hypotheses. This approach supports n-best hypotheses in the dialog manager and keeps track of the dialog state using a discourse interpretation algorithm with the agenda graph and focus stack. Given the agenda graph and n-best hypotheses, the system can pre...

When the top ASR hypothesis is incorrect, often the correct hypothesis is listed as an alternative in the ASR N-Best list. Whereas traditional spoken dialog systems have struggled to exploit this information, this paper argues that a dialog model that tracks a distribution over multiple dialog states can improve dialog accuracy by making use of the entire N-Best list. The key element of the app...

Dialog act segmentation and recognition are basic natural language understanding tasks in spoken dialog systems. This paper investigates a unified architecture for these two tasks, which aims to improve the model’s performance on both of the tasks. Compared with past joint models, the proposed architecture can (1) incorporate contextual information in dialog act recognition, and (2) integrate m...

In this paper we describe a systematic approach for creating a dialog management system based on a Construct Algebra, a collection of relations and operations on a task representation. These relations and operations are analytical components for building higher level abstractions called dialog motivators. The dialog manager, consisting of a collection of dialog motivators, is entirely built usi...

Statistical user simulation is an efficient and effective way to train and evaluate the performance of a (spoken) dialog system. In this paper, we design and evaluate a modular data-driven dialog simulator where we decouple the “intentional” component of the User Simulator from the Error Simulator representing different types of ASR/SLU noisy channel distortion. While the former is composed by ...

We present a new methodology of user simulation applied to the evaluation and refinement of stochastic dialog systems. Common weaknesses of these systems are the scarceness of the training corpus and the cost of an evaluation made by real users. We have considered the user simulation technique as an alternative way of testing and improving our dialog system. We have developed a new dialog manag...