Spoken Natural Language Dialog Systems

As spoken natural language dialog systems technology continues to make great strides, numerous issues regarding dialog processing still need to be resolved. This book presents an exciting new dialog processing architecture that allows for a number of behaviors required for effective human-machine interactions, including: problem-solving to help the user carry out a task, coherent subdialog movement during the problem-solving process, user model usage, expectation usage for contextual interpretation and error correction, and variable initiative behavior for interacting with users of differing expertise. The book also details how different dialog problems in processing can be handled simultaneously, and provides instructions and in-depth result from pertinent experiments. Researchers and professionals in natural language systems will find this important new book an invaluable addition to their libraries.

Dialog Processing Theory

Consider the following dialog situation. The computer is providing a human user with assistance in fixing an electronic circuit that causes a Light Emitting Diode (LED) to display in a certain way. The current focus of the task and dialog is to determine the status of a wire between labeled connectors 84 and 99, a wire needed for the circuit that is absent. Figures 3.1 and 3.2 show two possible dialog interactions that could occur at this point. In figure 3.1, the computer has total dialog control, and a total of 29 utterances are needed to guide the user through the rest of the dialog. In figure 3.2, the human user has overall dialog control, but the computer is allowed to provide direct assistance as needed (i.e. in helping add the wire). Only 11 utterances are needed for the experienced user to complete the dialog. These samples are from interactions with a working spoken natural language dialog system. To engage in such dialog interactions, a system must exhibit the behaviors mentioned at the beginning of chapter 1: (1) problem solving for providing task assistance, (2) conducting subdialogs to achieve appropriate subgoals, (3) exploiting user model to enable useful interactions, (4) exploiting context dependent expectations when interpreting user inputs, and (5) engaging in variable initiative dialogs. Achieving these behaviors while facilitating the measurement of system performance via experimental interaction requires a theory of dialog processing that integrates the following subtheories. • An abstract model of interactive task processing. • A theory about the purpose of language within the interactive task processing environment. • A theory of user model usage. • A theory of contextual interpretation. • A theory of variable initiative dialog. This chapter presents such a theory of dialog processing. Frequent reference to the dialog examples in figures 3.1 and 3.2 will guide the discussion. The first section discusses the overall system architecture that facilitates integrated dialog processing. The remainder of the chapter addresses each subtheory in turn, emphasizing how each fits into the overall architecture. The chapter concludes with a summary description of the integrated model.

Computational Model

This chapter describes the computational model that has evolved from the theory of integrated dialog processing presented in the previous chapter. The organization of this chapter follows. 1. A high-level description of the basic dialog processing algorithm. 2. A detailed discussion of the major steps of the algorithm. 3. A concluding critique that evaluates the model’s effectiveness at handling several fundamental problems in dialog processing. The system software that implements this model is available via anonymous FTP. Details on obtaining the software are given in appendix C. Figure 4.1 describes the basic steps of the overall dialog processing algorithm that is executed by the dialog controller. By necessity, this description is at a very high level, but specifics will be given in subsequent sections. The motivation for these steps is presented below. Since the computer is providing task assistance, an important part of the algorithm must be the selection of a task step to accomplish (steps 1 and 2). Because the characterization of task steps is a function of the domain processor, the dialog controller must receive recommendations from the domain processor during the selection process (step 1). However, since a dialog may have arbitrary suspensions and resumptions of subdialogs, the dialog controller cannot blindly select the domain processor’s recommendation. The relationship of the recommended task step to the dialog as well as the dialog status must be considered before the selection can be made (step 2). Once a task step is selected, the dialog controller must use the general reasoning facility (i.e. the interruptible theorem prover, IPSIM) in step 3 to determine when the task step is accomplished. Whenever the theorem prover cannot continue due to a missing axiom, the dialog controller uses available knowledge about linguistic realizations of utterances in order to communicate a contextually appropriate utterance as well as to compute expectations for the response. After the response is received and its relationship to the missing axiom determined, the dialog controller must decide how to continue the task step completion process.

Foundational Work in Integrated Dialog Processing

Building a working spoken natural language dialog system is a complex challenge. It requires the integration of solutions to many of the important subproblems of natural language processing. This chapter discusses the foundations for a theory of integrated dialog processing, highlighting previous research efforts. The traditional approach in AI for problem solving has been the planning of a complete solution. We claim that the interactive environment, especially one with variable initiative, renders such a strategy inadequate. A user with the initiative may not perform the task steps in the same order as those planned by the computer. They may even perform a different set of steps. Furthermore, there is always the possibility of miscommunication. Regardless of the source of complexity, the previously developed solution plan may be rendered unusable and must be redeveloped. This is noted by Korf [Kor87]: . . . Ideally, the term planning applies to problem solving in a real-world environment where the agent may not have complete information about the world or cannot completely predict the effects of its actions. In that case, the agent goes through several iterations of planning a solution, executing the plan, and then replanning based on the perceived result of the solution. Most of the literature on planning, however, deals with problem solving with perfect information and prediction. . . . Wilkins [W1184] also acknowledges this problem: . . . In real-world domains, things do not always proceed as planned. Therefore, it is desirable to develop better execution-monitoring techniques and better capabilities to replan when things do not go as expected. This may involve planning for tests to verify that things are indeed going as expected.... The problem of replanning is also critical. In complex domains it becomes increasingly important to use as much as possible of the old plan, rather than to start all over when things go wrong. . . . Consequently, Wilkins adopts the strategy of producing a complete plan and revising it rather than reasoning in an incremental fashion.

Enhanced Dialog Processing: Verifying Doubtful Inputs

Every natural language parser will sometimes misunderstand its input. Misunderstandings can arise from speech recognition errors or inadequacies in the language grammar, or they may result from an input that is ungrammatical or ambiguous. Whatever their cause, misunderstandings can jeopardize the success of the larger system of which the parser is a component. For this reason, it is important to reduce the number of misunderstandings to a minimum. In a dialog system, it is possible to reduce the number of misunderstandings by requiring the user to verify each utterance. Some speech dialog systems implement verification by requiring the user to speak every utterance twice, or to confirm a word-by-word readback of every utterance. Such verification is effective at reducing errors that result from word misrecognitions, but does nothing to abate misunderstandings that result from other causes. Furthermore, verification of all utterances can be needlessly wearisome to the user, especially if the system is working well. A superior approach is to have the spoken language system verify the deduced meaning of an input only under circumstances where the accuracy of the deduced meaning is seriously in doubt, or correct understanding is essential to the success of the dialog. The verification is accomplished through the use of a verification subdialog—a short sequence of conversational exchanges intended to confirm or reject the hypothesized meaning. The following example of a verification subdialog will suffice to illustrate the idea. . . . computer: What is the LED displaying? user: The same thing. computer: Did you mean to say that the LED is displaying the same thing? user: Yes. . . . As will be further seen below, selective verification via a subdialog results in an unintrusive, human-like exchange between user and machine. A recent enhancement to the Circuit Fix-it Shop dialog system is a subsystem that uses a verification subdialog to verify the meaning of the user’s utterance only when the meaning is in doubt or when accuracy is critical for the success of the dialog. Notable features of this new verification subsystem include the following.