The Correction of Ill-Formed Input Using History-Based Expectation with Applications to Speech Understanding

A method for error correction of ill-formed input is described that acquires dialogue patterns in typical usage and uses these patterns to predict new inputs. Error correction is done by strongly biasing parsing toward expected meanings unless clear evidence from the input shows the current sentence is not expected. A dialogue acquisition and tracking algorithm is presented along with a description of its implementation in a voice interactive system. A series of tests are described that show the power of the error correction methodology when stereotypic dialogue occurs.

[1]  Norman K. Sondheimer,et al.  Relaxation Techniques for Parsing Grammatically III-Formed Input in Natural Language Understanding Systems , 1981, Am. J. Comput. Linguistics.

[2]  B. H. Thompson,et al.  Linguistic Analysis of Natural Language Communication With Computers , 1980, COLING.

[3]  Jean-Marie Pierrel,et al.  Organization and operation of a connected speech understanding system at lexical, syntactic and semantic levels , 1976, ICASSP.

[4]  Patrick Henry Winston,et al.  Learning structural descriptions from examples , 1970 .

[5]  Donald E. Walker,et al.  The SRI speech understanding system , 1975 .

[6]  Alan W. Biermann,et al.  Computer control via limited natural language , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[7]  Bruce Wilson Ballard,et al.  Semantic and procedural processing for a natural language programming system. , 1979 .

[8]  Christopher K. Riesbeck,et al.  Comprehension by computer : expectation-based analysis of sentences in context , 1976 .

[9]  Alan W. Biermann,et al.  Automatic Program Construction Techniques , 1984 .

[10]  Pamela Kennedy Fink.  The acquisition and use of dialogue expectation in speech recognition (natural language processing, artificial intelligence) , 1983 .

[11]  Roger C. Schank,et al.  SCRIPTS, PLANS, GOALS, AND UNDERSTANDING , 1988 .

[12]  William A. Woods,et al.  Computational Linguistics Transition Network Grammars for Natural Language Analysis , 2022 .

[13]  Barbara J. Grosz,et al.  Understanding Spoken Language , 1978 .

[14]  J. Wolf,et al.  The HWIM speech understanding system , 1977 .

[15]  Tai-Ping Ho.  The dialogue designing dialogue system (user interface, natural language programming) , 1984 .

[16]  Victor R. Lesser,et al.  The Hearsay-II Speech-Understanding System: Integrating Knowledge to Resolve Uncertainty , 1980, CSUR.

[17]  Ryszard S. Michalski,et al.  Pattern Recognition as Rule-Guided Inductive Inference , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Alan W. Biermann,et al.  Constructing Programs from Example Computations , 1976, IEEE Transactions on Software Engineering.

[19]  Lance A. Miller,et al.  Parse Fitting and Prose Fixing: Getting a Hold on III-Formedness , 1983, Am. J. Comput. Linguistics.

[20]  Norman K. Sondheimer,et al.  Meta-Rules as a Basis for Processing III-Formed Input , 1983, Am. J. Comput. Linguistics.

[21]  Ralph M. Weischedel,et al.  Responding Intelligently to Unparsable Inputs , 1980, Am. J. Comput. Linguistics.

[22]  Jaime G. Carbonell,et al.  Recovery Strategies for Parsing Extragrammatical Language , 1983, CL.

[23]  Tai-Ping Ho.  The Dialogue Designing Dialogue System , 1984 .

[24]  Wayne A. Lea,et al.  Trends in Speech Recognition , 1980 .

[25]  Alan W. Biermann,et al.  Natural language with discrete speech as a mode for human-to-machine , 1985, CACM.

[26]  Alan W. Biermann,et al.  Toward Natural Language Computation I , 1980, CL.

[27]  Bruce Lowerre,et al.  The Harpy speech understanding system , 1990 .

[28]  Ehud Shapiro,et al.  Algorithmic Program Debugging , 1983 .

[29]  Richard Granger.  The NOMAD System: Expectation-Based Detection and Correction of Errors During Understanding of Syntactically and Semantically III-Formed Text , 1983, Am. J. Comput. Linguistics.