Enhanced Dialog Processing: Verifying Doubtful Inputs

1995 ◽  
Author(s):  
Ronnie W. Smith ◽  
D. Richard Hipp
Keyword(s):  
Speech Recognition ◽  
Natural Language ◽  
Spoken Language ◽  
Short Sequence ◽  
Dialog Systems ◽  
Dialog System ◽  
Superior Approach ◽  
Correct Understanding ◽  
Recognition Errors ◽  
Dialog Processing

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.

Extending the State of the Art

1995 ◽  
Author(s):  
Ronnie W. Smith ◽  
D. Richard Hipp
Keyword(s):  
Natural Language ◽  
Current System ◽  
Plan Recognition ◽  
Behavioral Differences ◽  
Dialog Systems ◽  
Dialog System ◽  
Ongoing Work ◽  
Language User ◽  
Satisfactory Answer ◽  
Dialog Processing

This book has presented a computational model for integrated dialog processing. The primary contributions of this research follow. • A mechanism (the Missing Axiom Theory) for integrating subtheories that each address an independently studied subproblem of dialog processing (i.e. interactive task processing, the role of language, user modeling, and exploiting dialog expectation for contextual interpretation and plan recognition). • A computational theory for variable initiative behavior that enables a system to vary its responses at any given moment according to its level of initiative. • Detailed experimental results from the usage of a spoken natural language dialog system that illustrate the viability of the theory and identify behavioral differences of users as a function of their experience and initiative level. This chapter provides a concluding critique, which identifies areas of ongoing work and offers some advice for readers interested in developing their own spoken natural language dialog systems. This section describes important issues we did not successfully address in this research because either (1) we studied the problem but do not as yet have a satisfactory answer; or (2) it was not necessary to investigate the problem for the current system. Regardless of the reason, incorporating solutions to these problems is needed to strengthen the overall model. In section 4.7.3 we have already discussed the difficulties in determining when and how to change the level of initiative during a dialog as well as the problems in maintaining coherence when such a change occurs. Ongoing work in this area is being conducted by Guinn [Gui93]. His model for setting the initiative is based on the idea of “evaluating which participant is better capable of directing the solution of a goal by an examination of the user models of the two participants.” He provides a formula for estimating the competence of a dialog participant based on a probabilistic model of the participant’s knowledge about the domain. Using this formula, Guinn has conducted extensive experimental simulations testing four different methods of selecting initiative.

Spoken Natural Language Dialog Systems

1995 ◽  
Author(s):  
Ronnie W. Smith ◽  
D. Richard Hipp
Keyword(s):  
Problem Solving ◽  
Natural Language ◽  
Error Correction ◽  
User Model ◽  
Dialog Systems ◽  
Problem Solving Process ◽  
Processing Architecture ◽  
Dialog Processing

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.

YAG: A TEMPLATE-BASED TEXT REALIZATION SYSTEM FOR DIALOG

2001 ◽  
Vol 09 (06) ◽  
pp. 649-659
Author(s):  
SONGSAK CHANNARUKUL ◽  
SUSAN W. MCROY ◽  
SYED S. ALI
Keyword(s):  
Natural Language ◽  
Real Time ◽  
Semantic Content ◽  
Text Structure ◽  
Tutoring Systems ◽  
Dialog Systems ◽  
Base Content ◽  
Knowledge Based ◽  
Text Structures ◽  
Dialog System

We present a natural language realization component, called YAG, that is suitable for intelligent tutoring systems that use dialog. Dialog imposes unique requirements on a generation component, namely: dialog systems must interact in real-time; they must be capable of producing fragmentary output; and they may be re-deployed in a number of different domains. Our approach to real-time natural language realization combines a declarative, template-based approach for the representation of text structure with knowledge-based methods for representing semantic content. Possible text structures are defined in a declarative language that is easy to understand, maintain, and re-use. A dialog system can use YAG to realize text structures by specifying a template and content from its knowledge base. Content can be specified in one of two ways: (1) as a sequence of propositions along with some control features; or (2) as a set of feature-value pairs. YAG's template realization algorithm realizes text without any search (in contrast to systems that must find rules that unify with a feature structure).

scholarly journals Are Neural Open-Domain Dialog Systems Robust to Speech Recognition Errors in the Dialog History? An Empirical Study

2020 ◽  
Author(s):  
Karthik Gopalakrishnan ◽  
Behnam Hedayatnia ◽  
Longshaokan Wang ◽  
Yang Liu ◽  
Dilek Hakkani-Tür
Keyword(s):  
Speech Recognition ◽  
Empirical Study ◽  
Open Domain ◽  
Dialog Systems ◽  
Recognition Errors

A Voice Calculator Based on Speech Error Correction

2013 ◽  
Vol 846-847 ◽  
pp. 1239-1242
Author(s):  
Yang Yang ◽  
Hui Zhang ◽  
Yong Qi Wang
Keyword(s):  
Natural Language Processing ◽  
Numerical Calculation ◽  
Speech Recognition ◽  
Natural Language ◽  
Error Correction ◽  
Recent Work ◽  
Language Processing ◽  
Speech Error ◽  
Parsing Algorithm ◽  
Recognition Errors

This paper presents our recent work towards the development of a voice calculator based on speech error correction and natural language processing. The calculator enhances the accuracy of speech recognition by classifying and summarizing recognition errors on numerical calculation speech recognition area, then constructing Pinyin-text-mapping library and replacement rules, and combing priority correction mechanism and memory correction mechanism of Pinyin-text-mapping. For the expression after correctly recognizing, the calculator uses recursive-descent parsing algorithm and synthesized attribute computing algorithm to calculate the final result and output the result using TTS engine. The implementation of this voice calculator makes a calculator more humane and intelligent.

Foundational Work in Integrated Dialog Processing

1995 ◽  
Author(s):  
Ronnie W. Smith ◽  
D. Richard Hipp
Keyword(s):  
Problem Solving ◽  
Natural Language ◽  
Language Processing ◽  
Real World ◽  
Traditional Approach ◽  
Complete Solution ◽  
Execution Monitoring ◽  
Dialog System ◽  
Foundational Work ◽  
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.

Application-Independent Knowledge-Processing in a Task-Oriented Speech-Dialog-System (Applikationsunabhängige Wissensverarbeitung in aufgabenorientierten Sprach-Dialogsystemen)

2004 ◽  
Vol 46 (6) ◽  
Author(s):  
Jürgen te Vrugt ◽  
Thomas Portele
Keyword(s):  
Building Blocks ◽  
Spoken Language ◽  
Knowledge Sources ◽  
Knowledge Processing ◽  
Control Applications ◽  
Dialog Systems ◽  
Dialog System ◽  
Run Time ◽  
Task Oriented

SummarySpoken language dialog systems allow users to control applications by voice. These systems tightly integrate the applications to control them, even though knowledge sources of the building blocks are often configurable. Some dialog systems controlling multiple applications loosen the coupling.This article introduces a dialog system accessing multiple applications with a dynamic setup that can be changed at run-time, separating the applications from the system. This is achieved by application-independent knowledge processing inside the dialog system based on modular ontological descriptions. A clear interface between dialog system and applications is provided, generic dialog functionality is realized on top of the application independent knowledge processing. Examples illustrate interactions with the system.

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