Abstract:
A method and system, which may be implemented by employing a program storage device readable by machine, and tangibly embodying a program of instructions executable by the machine to perform method steps for ensuring robustness of a natural language understanding (NLU) system, includes tagging recognized words of a command input to the NLU system to associate the command with a context, and translating the command to at least one formal command based on the tagged words. A top ranked formal command is determined based on scoring of the tagged recognized words and scoring translations of the at least one formal command. Whether the top ranked formal command is accepted is determined by comparing a feature vector of the top ranked formal command to representations of feature vectors stored in an accept model. The top ranked formal command is executed if accepted and incorrect commands are prevented from execution to provide a robust NLU system.

Description:
BACKGROUND OF THE INVENTION 
   1. Field of the Invention 
   The present invention relates to computer systems with natural language understanding capabilities, and more particularly to a method and system for ensuring robustness for these systems. 
   2. Description of the Related Art 
   For computer systems with natural language understanding (NLU) capabilities, errors can be made by the system in translating the user&#39;s input, resulting in an incorrect action being executed by the system. Presently, a typical natural language understanding system which receives a command which is incorrect carries out the command. If the system carries out this action, problems may be encountered. For example, data may be changed, memory updated or erased, or other detrimental events may occur. The occurrence of these events may require undoing the previous command or redoing a sequence of commands to return the system to a desired state. This results in lost time and annoyance of the user. 
   Therefore, a need exists for a system and method for ensuring robustness in natural language understanding by determining incorrect commands and preventing their execution. 
   SUMMARY OF THE INVENTION 
   A method, which may be implemented by employing a program storage device readable by machine, and tangibly embodying a program of instructions executable by the machine to perform method steps for ensuring robustness of a natural language understanding (NLU) system, includes tagging recognized words of a command input to the NLU system to associate the command with a context, and translating the command to at least one formal command based on the tagged words. A top ranked formal command is determined based on scoring of the tagged recognized words and scoring translations of the at least one formal command. Whether the top ranked formal command is accepted is determined by comparing a feature vector of the top ranked formal command to representations of feature vectors stored in an accept model. The top ranked formal command is executed if accepted and incorrect commands are prevented from execution to provide a robust NLU system. 
   In other methods, which may be implemented by employing the program storage device, the step of determining a top ranked formal command may include the step of ranking formal commands based on a product of scores of the tagged words and scores of translations of the at least one formal command. The step of determining a top ranked formal command may include the step of ranking N formal commands where N is a selectable system parameter. The step of determining whether the top ranked formal command is rejected by comparing the feature vector of the top ranked formal command to feature vector representations stored in a reject model may be included. The step of providing the reject model by including representations of feature vectors of formal commands corresponding to words or sentences to be rejected may also be included. 
   The reject model may include a cluster of models. The step of clustering the cluster of models based on at least one of mistakes in commands, mistakes in arguments of the command, and processing mistakes may further be included. The method steps may include providing the accept model by including representations of feature vectors of formal commands corresponding to words or sentences to be accepted. The step of determining whether the top ranked formal command is accepted may include the step of computing a probability of acceptance for the command. The step of computing a probability of acceptance for the command may include the steps of computing a probability of rejection for the command and comparing the probability of acceptance to the probability of rejection to determine if the command is to be executed. The step of computing a probability of acceptance for the command may include the step of comparing the probability of acceptance to a threshold probability to determine if the command is to be executed. The threshold may be modified by the user. 
   In other method steps, the accept model may include a cluster of models. The step of clustering the cluster of models based on at least one of mistakes in commands, mistakes in arguments of the command, and processing mistakes may be included. The step of preventing incorrect commands from execution to provide a robust NLU system, may include executing a do nothing command responsive to the incorrect commands. 
   A method for building an evaluation corpus for checking commands in a natural language understanding (NLU) system includes providing a training corpus of words and sentences. The words and sentence have a user input form and a corresponding formal command associated with the user input form. At least some of corresponding formal commands include a do nothing command for incomplete and/or incorrect commands. The words and sentences of the training corpus are passed to the natural language understanding system to determine a top ranked command. The top ranked command is compared to the corresponding formal command to determine if a match exists. If a match exists, the word or sentence is placed in the accept corpus, otherwise in the reject corpus. Features from the words or sentences of the accept corpus and the reject corpus are extracted to construct a feature vector for each word or sentence, and an accept model and a reject model are constructed from the extracted feature vectors. 
   In other methods, the feature vectors may include tagging scores for recognized words of the word or sentence represented by the feature vectors or translation scores for formal commands associated with the word or sentence represented by the feature vector. The feature vectors may include a do nothing score associated with words and sentences. The do nothing score indicates a probability that the do nothing command is present for associated words and sentences. The feature vectors may include a top command similarity measure for counting identical formal commands, and/or a parameter mismatch feature for measuring a number of command arguments in a translation of a command. The method may further include the step of clustering feature vectors according to selected characteristics and conditions to provide at least one of a cluster of accept models and a cluster of reject models. The accept model and the reject model may consist of mean vectors and covariance matrices of feature vectors representing the words and sentences and a number of words and sentences stored in the model. 
   A natural language understanding (NLU) system includes a tagger adapted for tagging recognized words of a command input to the NLU system to associate the command with a context, and a translator adapted for translating the command to at least one formal command based on the tagged words. A robustness checker is included for determining a top ranked formal command based on scoring of the tagged recognized words and scoring translations of the at least one formal command. The robustness checker determines whether the top ranked formal command is accepted by comparing a feature vector of the top ranked formal command to feature vector representations stored in an accept model. A command executor executes the top ranked formal command if accepted and prevents incorrect commands from execution to provide a robust NLU system. 
   In alternate embodiments, the top ranked formal command may be ranked based on a product of scores of the tagged words and translation scores of the at least one formal command. The top ranked formal command may be selected from a ranked list of N formal commands where N is a selectable system parameter. An accept model for storing feature vector representations of accept commands may be included, and the feature vector representations may be employed to determine an acceptance probability of formal commands. The accept model may include a cluster of models. The system may include a reject model for storing feature vector representations of reject commands. The feature vector representations are employed to be compared to a rejection probability of formal commands. The reject model may include a cluster of models. The robustness checker may include a feature extractor for extracting feature vectors from the command input. The feature vectors may include tagging scores for recognized words of a word or sentence represented by the feature vector and/or translation scores for formal commands associated with the word or sentence represented by the feature vector. The feature vectors may also include a do nothing score associated with words and sentences. The do nothing score for indicates a probability that a do nothing command is present for associated words and sentences. The do nothing command is associated with incomplete or incorrect commands which may be input as a command. The feature vectors may include a top command similarity measure for counting identical formal commands and/or a parameter mismatch feature for measuring a number of command arguments in a translation of a command. The robustness checker may include a robustness evaluator for determining whether the top ranked formal command is accepted by comparing the top ranked formal command to feature vectors stored in an accept model. 
   A natural language understanding system includes a corpus of rejectable commands corresponding to incorrect commands capable of being input by a user and do nothing commands corresponding to the incorrect commands input to the system which maintain the system in an idle state and/or prompt the user to input a response. 
   These and other objects, features and advantages of the present invention will become apparent from the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings. 

   
     BRIEF DESCRIPTION OF DRAWINGS 
     The invention will be described in detail in the following description of preferred embodiments with reference to the following figures wherein: 
       FIG. 1  is a block/flow diagram of a system/method for a natural language understanding (NLU) system employing a robustness checker in accordance with the present invention; 
       FIG. 2  is an illustrative example of output from a tagger and a translator in accordance with the present invention; 
       FIG. 3  is a block/flow diagram illustratively showing the construction of an accept corpus and a reject corpus in accordance with the present invention; 
       FIGS. 4A and 4B  are block/flow diagrams showing a system/method for constructing accept and reject models in accordance with the present invention; 
       FIG. 5  is a block/flow diagram showing a robustness checker in accordance with the present invention; 
       FIGS. 6A and 6B  are block/flow diagrams showing a system/method for clustering accept and reject models in accordance with the present invention; and 
       FIG. 7  is a block/flow diagram showing a robustness checker employing clustered models in accordance with the present invention. 
   

   DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
   The present invention ensures robustness in computer systems by automatically recognizing the inputs that are likely to result in an incorrect action, and taking preventive measures. For natural language systems, where a command is misinterpreted or incorrectly given, the present invention determines the incorrectness of the command prior to its execution and takes appropriate action. For example, incorrectly interpreted utterances are identified and mapped to a “do nothing” command, and thus the execution of a potentially incorrect command is avoided. The system can remain idle for a do nothing command or the system may prompt a user for more information. 
   Although confidence scoring for speech recognition systems is employed in recognizing speech, these solutions do not address the errors caused by the natural language understanding systems, do not use feature extraction based on natural language understanding, and do not have a “do nothing” command built-in explicitly into the system. These solutions are also specific to systems with spoken input only, and are not relevant to systems where the input may have come from another source, such as a typed input or a handwritten input. The present invention accommodates input from a plurality of different sources for example, handwriting recognition devices, speech recognition systems, typed text. Input devices may include a variety of devices such as, telephones, personal digital assistants, computers, etc. 
   The invention provides a method and apparatus to build a statistically trainable system, which will be called a robustness checker, hereinafter, that is part of a natural language understanding system, and is capable of recognizing user inputs that are likely to have been incorrectly translated by a natural language understanding (NLU) system. The robustness checker is statistically trained using the same training data that is used to build models for the NLU system. The NLU system may include a tagger component and a translator component. When deployed as part of a real-time conversational interface, the NLU systems will produce the best choice, along with N-1 other choices (for a total of N choices) for the formal command corresponding to the user&#39;s input. The training data will include utterances for which no legal formal language statement exists, and these utterances will be mapped to a “do_nothing” command. 
   All the training data that was used to build the NLU models are subjected to the NLU, and on the basis of the results, the training sentences may be divided into two categories: the accept category, and the reject category. The accept category includes all the sentences for which the NLU&#39;s first choice of formal command is correct, and the reject category includes all the sentences for which the first choice of formal command is not correct. 
   A feature vector is computed for each of the utterances. The feature vector may include one or more of the following: tagger scores, translator scores, the normalized score for the “do_nothing” command for each tagging, a command similarity measure of the N choices of formal commands from the NLU, and a parameter mismatch factor between the tagger output and the translator output. For each of the accept and reject categories, statistical models are built using the feature vectors from that category. These models are used by the robustness checker to make a decision on whether to accept or reject a formal command produced by the NLU corresponding to a new user input. When appropriate, more than one model may be built for each category to allow for clustered-modeling. 
   When deployed as part of a real-time system, the robustness checker will first calculate the feature vector corresponding to each input sentence from the user, and map the formal command produced by the NLU for that input sentence to either the accept category or the reject category. If the formal command is mapped to the accept category, then the formal command is submitted for execution. If the command is mapped to the reject category, then it is rejected and the user may be asked to repeat the input. 
   It should be understood that the elements shown in FIGS.  1  and  3 – 7  may be implemented in various forms of hardware, software or combinations thereof. Preferably, these elements are implemented in software on one or more appropriately programmed general purpose digital computers having a processor and memory and input/output interfaces. Referring now to the drawings in which like numerals represent the same or similar elements and initially to  FIG. 1 , a block/flow diagram an NLU system  10  and method employing a robustness checker is shown in accordance with the present invention. A user (or users) submits an input to the system  10  via user interface  100 . The input from user may be in text, either typed or recognized from speech or recognized or generated from another medium. The input may also be in spoken/acoustic form or handwritten form. If the input is in text form, such as an input submitted using a computer keyboard or personal digital assistant/telephone keypad, then the text is sent to tagger  200 . If the input is not in text form, such as a spoken input or a handwritten input, then the input (from a speaker, acoustic signal or visual signal) is translated to text form using speech recognition or handwriting recognition in block  150 . The recognized text is then sent to tagger  200 . Techniques for speech recognition and handwriting recognition may be employed as known in the art. 
   Tagger  200  is responsible for recognizing classes of words and phrases present in the input sentence. For example, in a natural language interface to an electronic mail application, the user&#39;s input may be:
         forward this message to David
 
The corresponding output from tagger  200  may be:
   forward this message to NAME
 
where “David” has been tagged as “NAME”. Tagger  200  may be built using statistical parsing techniques, such as those described in Ward, T., et al., “Towards Speech Understanding Across Multiple Languages,” International Conference on Spoken Language Processing, Sydney, Australia, December 1998, incorporated herein by reference.
       

   The output from tagger  200  is sent to translator  300  which will assign the tagged sentence to a formal command. For example, the illustrative sentence described above may be assigned to a formal command of the form:
         forward_message(message=current, recipient=NAME)
 
Translator  200  may be built using the techniques described in Papineni, K., et al., “Feature-Based Language Understanding”, Eurospeech, Rhodes, Greece, September 1997, incorporated herein by reference.
       

   Referring to  FIG. 2 , an illustrative example of the output from tagger  200  and translator  300  is shown. In this example, the user&#39;s input is “close the message log folder”, and tagger  200  produces three possible tagged outputs  202 , and associated scores  204 . For example, the first choice of tagger  200  is “CLOSE the FNAME FOLDER” with a score of 0.873911, which also happens to be the correct tagging for this sentence. In this example, for each of the three tagged sentences, the translator  300  produces five choices of formal commands  302  and associated scores  304 . The first choice of translator  300  for the first tagged sentence is “close_folder(name=FNAME)” with a score of 0.96887374, which also happens to be the correct translation for this sentence. The selected formal command is “close_folder(name=“message log”), with a final score of 0.846709646214, which is calculated as the multiplicative product of the scores from tagger  200  and translator  300 . In this example, tagger  200  produces three choices, and translator  300  produces five choices for each of the three choices from tagger  200 , for a total of fifteen choices. In the general case, there will be N choices of formal commands, and the value of N is a system parameter. 
   In one embodiment of the invention, N is fifteen, as in the example given in  FIG. 2 . The final choice of the formal command is selected using the tagger output and the translator output for which the combined multiplicative score is the highest, and this command will be called the top ranking formal command. 
   Returning to  FIG. 1 , the output from the tagger  200  and translator  300 , which may be of the form given in  FIG. 2 , is sent to a robustness checker  400 . The robustness checker  400  is responsible for determining if the top ranking formal command should be accepted or rejected. If the command is to be accepted, then it is sent to command executor  500  and the command is executed. If the command is to be rejected, then the command is not executed and the user may have to resubmit the input, perhaps using a different choice of words. 
   Referring to  FIG. 3 , an illustrative block/flow diagram shows building accept and reject corpora, according to the present invention. A training corpus  601  includes data that is used for training tagger  200   a  and translator  300   a  models. 
   The contents of training corpus  601  include both input sentences and the associated formal commands. In a natural language interface for an electronic mail application, for example, the contents of training corpus  601  may illustratively include the following:
         do I have any new mail // check_new_mail( )   forward this to David // forward_message(message=current, recipient=David)   do I have any uh err // do_nothing( )   it is a nice day // do_nothing( )
 
The left side (before the “//” sign) includes the actual input sentences from the user, and the right side includes a corresponding correct formal command. In accordance with the present invention, some of the sentences are mapped to a “do_nothing” command, either because they are clearly out of the domain, or because they are not complete enough to form a command, etc. The “do_nothing” command is one way robustness is ensured for the natural language system  10 . All of the sentences in the training corpus  601  are subjected to tagger  200   a  and translator  300   a . Tagger  200   a  is functionally equivalent to tagger  200  from  FIG. 1 , and translator  300   a  is functionally equivalent to translator  300  from  FIG. 1 . For each sentence in the training corpus  601 , the output of the translator  300   a  is examined by the translation checker  600 . If the output is correct (i.e. the correct formal command is selected by the system which corresponds to the correct command in the training corpus), then the sentence is added to the accept corpus  602 . If the output is incorrect, then the sentence is added to the reject corpus  603 .
       

   Referring to  FIGS. 4A and 4B , an example of the process and system for building accept and reject models are illustratively depicted, according to the present invention. Each of the sentences in the accept corpus  602  and the reject corpus  603  are subjected to feature extractors  401   a  and  401   b , respectively, both of which are functionally equivalent to each other. The feature extractors  401   a  and  401   b  are responsible for extracting a set of features for each sentence, and constructing a feature vector, v. 
   In one embodiment of the invention, the following features are used to construct the feature vector, v. All tagging scores and all translation scores from the output are included (see  FIG. 2 ) in the feature vector, v. The next j features are the normalized cumulative do_nothing scores for taggings 1 through j, where j is the total number of taggings. The do_nothing score for tagging j could be written as 
               DNS   j     =       ∑     i   =   1       k   j       ⁢     1       l   ij     +   1                 (   1   )             
 
where l ij  is the translation rank of the ith do_nothing command in tagging and k j  is the number of do_nothing translations of tagging j. In the example of  FIG. 2 , three taggings are given. DNS 1 , DNS 2  and DNS 3  are calculated. The do_nothing score increases when more do_nothing translations are present in a tagging or when a do_nothing translation is assigned a higher ranking in the translations of a tagging.
 
   Another feature is a top command similarity measure, TCS. This TCS feature counts the number of formal commands that are identical to the top command of the top tagging, without considering the arguments of the command. For the example in  FIG. 2 , close_folder(name=FNAME) and close_folder (folder=current) would be considered identical. This feature can be written as 
             TCS   =       ∑     i   =   1     k     ⁢     1       k   i     +   1                 (   2   )             
 
where k i  is the index of the ith translation that is identical to the first, and k is the number of translations identical to the first tagging.
 
   Another feature includes a parameter mismatch feature, which measures the number of command arguments that are present in the translation of a command, but are not available in the tagging of that command. For example, in  FIG. 2 , where the selected formal command is close_folder (name=FNAME) and the argument FNAME maps to “message log”, the parameter mismatch would be 0. On the other hand, if none of the words were tagged as FNAME then argument will be incomplete, and in this case the parameter mismatch would be 1. For commands with more than one argument, the parameter mismatch may be greater than 1. 
   The feature extractor  401   a  extracts the features and constructs the feature vector for each sentence and submits the feature vector to a model constructor  604   a . Similarly, the feature extractor  401   b  submits the feature vector to model constructor  604   b . Model constructors  604   a  and  604   b  are functionally equivalent to each other. In one embodiment of the invention, model constructor  604   a  computes an accept mean vector m A  and accept covariance matrix Σ A  for all the feature vectors produced by the feature extractor  401   a , corresponding to all the sentences in the accept corpus  602 . An accept model  605  includes the accept mean vector m A , the accept covariance matrix Σ A , and the total number of sentences n A  in the accept corpus  602 . Similarly, model constructor  604   b  computes a reject mean vector m R  and reject covariance matrix Σ R  for all the feature vectors produced by the feature extractor  401   b , corresponding to all the sentences in the reject corpus  603 . The reject model  605  includes the reject mean vector m R , the reject covariance matrix Σ R , and the total number of sentences n R  in the reject corpus  603 . 
   Referring to  FIG. 5 , a robustness checker  400  is schematically shown in accordance with the present invention. Robustness checker  400  connects to translator  300 . Robustness checker  400  include feature extractor  401 , robustness evaluator  402 , accept model  605  and reject model  606 . Feature extractor  401  is functionally equivalent to feature extractors  401   a  and  401   b  described previously with reference to  FIGS. 4A and 4B . 
   Robustness evaluator  402  is responsible for evaluating a given feature vector, calculated by feature extractor  401  using the output from translator  300  for a new sentence from the user, and determining if the corresponding formal command should be accepted (i.e. submitted for execution), or rejected. 
   The robustness evaluator  402  first calculates P(A), the prior probability for command acceptance, and P(R), the prior probability for command rejection. In one embodiment of the invention, P(A) and P(R) are calculated using
 
 P ( A )= n   A /( n   A   +n   R )  (3)
 
 P ( R )= n   R /( n   A   +n   R )  (4)
 
where n A  and n R  are the number of sentences in the accept corpus  602  and reject corpus  603 , respectively.
 
   Given a feature vector v produced by the feature extractor  401 , the robustness evaluator  402  calculates the conditional probabilities P(v|A) and P(v|R), using 
               P   ⁡     (     v   |   A     )       =       ⅇ       -     1   2       ⁢       (     v   -     m   A       )     T     ⁢       ∑   A     -   1       ⁢     (     v   -     m   A       )                 (     2   ⁢   π     )         n   A     2       ⁢            ∑   R            1   2                   (   5   )                 P   ⁡     (     v   |   R     )       =       ⅇ       -     1   2       ⁢       (     v   -     m   R       )     T     ⁢       ∑   R     -   1       ⁢     (     v   -     m   R       )                 (     2   ⁢   π     )         n   R     2       ⁢            ∑   R            1   2                   (   6   )             
 
   T represents the transpose operator for vector v-m A  and Σ −1  is an inverse matrix. The classification rule is simple. A formal command with feature vector v is accepted if:
 
 P ( A ) P ( v|A )&gt; P ( R ) P ( v|R )  (7)
 
and rejected otherwise. If the command is to be executed, then it is submitted to a command executor  500 .
 
   Variations to the classification mechanism specified by Equations (3)–(7) above will now be described in greater detail. One variation is to permit the user to modify the values of P(A) and P(R). In one embodiment of the invention, the user interface  100  ( FIG. 1 ) permits the user to modify the values of P(A) and P(R). By changing the values of P(A) and/or P(R), users can modify the system behavior to suit desired preference. For example, it the user feels that too many commands are being rejected by the system, then increasing the value of P(A) (or equivalently, decreasing the value of P(R)) would increase the acceptance rate of the commands. An extreme case is when the value of P(R) is set to 0, where all commands will be accepted. Once the user modifies the values for P(A) and/or P(R), the classification rule of EQ. (7) is applied with the new values. 
   Another variation includes not using the reject model  606  at all, and basing all decisions on the accept model  605  only. With this variation, the reject model  606  is not constructed, and the formal command will be accepted if
 
 P ( A ) P ( v|A )&gt; P   th   (8)
 
where P th  is a predetermined threshold. Determining the value for P th  is subjective and may be defined using trial-and-error experiments or other determination techniques. The designer of the system may choose a default value for P th  by trying out different values for P th , and choose a value that gives a reasonable or desirable system behavior. Again, the user may be permitted to modify the value for P th  via the user interface  100 .
 
   Another variation includes the use of clustered accept and reject models. Referring to  FIGS. 6A and 6B , schematically illustrated is an example of a process for building clustered accept and reject models, in accordance with the present invention. In this embodiment, a plurality of accept models  605  and/or reject models  606  are employed. This embodiment may be adjusted to accommodate different sets of conditions that may lead to acceptance or rejection of a command. For example, a command may be rejected due to processing errors, e.g., because of an error made by the tagger  200  ( FIG. 1 ) or because of an error made by the translator  300  ( FIG. 1 ). Further, different errors may be distinguished based on the type of error. For example, the command itself could be wrong, or perhaps the command is correct but the arguments are incorrect. 
   The training data from accept corpus  602  is partitioned into one or more accept clusters  608  by a cluster assigner  607   a  which is coupled to the feature extractor  401   a . A similar operation is performed for the reject corpus  603  by a cluster assigner  607   b  which is coupled to the feature extractor  401   b . The number of clusters to be used in each case is a system parameter that needs to be selected by the designer of the system, which may be based on trial-and-error experiments or other methods. 
   In one embodiment of the invention, 3 clusters for both the accept clusters  608  and the reject clusters  609  (the number of clusters does not have to be the same for the accept and reject models). Assigning the data to one of the clusters can be done using rules (e.g. what kind of error resulted in the rejection), or using clustering techniques such as a K-means clustering algorithm, which is known in the art. Once the data is partitioned into the desired number of clusters, model constructors  604   a  and  604   b  construct the desired number of accept models  605  and reject models  606 . Each model will have its own mean vector, covariance matrix and number of sentences. For example, if we have 3 accept models, then accept model 1 will have accept mean vector m A1 , covariance matrix Σ A1  and number of sentences n A1 , and similarly for accept models 2 and 3 (and reject models as well). 
   Referring to  FIG. 7 , a robustness checker is illustratively shown employing clustered models in accordance with the present invention. Robustness evaluator  402  of robustness checker  400  calculates the values P(A j ) for each accept model j using:
 
 P ( A   j )= n   Aj   /n   total   (9)
 
where n Aj  is the number of sentences in accept cluster j, and n total  is the total number of sentences in all of the accept clusters and reject clusters. Similarly, robustness evaluator  402  calculates the values P(R j ) for each reject model k using:
 
 P ( R   k )= n   Rk   /n   total   (10)
 
where n Rk  is the number of sentences in reject cluster k, and n total  is the total number of sentences in all of the accept clusters and reject clusters.
 
   Given a feature vector v produced by the feature extractor  401 , the robustness evaluator  402  calculates the conditional probabilities P(v|A j ) and P(v|R k ) for each accept cluster j and reject cluster k, using: 
               P   ⁡     (     v   |     A   j       )       =       ⅇ       -     1   2       ⁢       (     v   -     m     A   j         )     T     ⁢       ∑     A   j       -   1       ⁢     (     v   -     m     A   j         )                 (     2   ⁢   π     )         n     A   j       2       ⁢            ∑     A   j              1   2                   (   11   )                 P   ⁡     (     v   |     R   k       )       =       ⅇ       -     1   2       ⁢       (     v   -     m     R   k         )     T     ⁢       ∑     R   k       -   1       ⁢     (     v   -     m     R   k         )                 (     2   ⁢   π     )         n     R   k       2       ⁢            ∑     R   k              1   2                   (   12   )             
 
The classification rule is as follows. A formal command with feature vector v is accepted if the
 
max( P ( A   j ) P ( v|A   j ))(over all  j )&gt;max( P ( R   k ) P ( v|R   k ))(over all  k )  (13)
 
and rejected otherwise. If the command is to be executed, it is submitted to the command executor  500  ( FIG. 1 ) as described above.
 
   The present invention presents a robustness checker that can be employed by a natural language understanding system to determine if the user&#39;s natural language input has be understood correctly by the system. The robustness checker is statistically trained using the same training data that was used to build the natural language understanding system. The robustness checker calculates a feature vector for each input from the user, compares the feature vector against a set of statistical models and determines if the input should be accepted or rejected. When the input is accepted, the command associated with the input is executed. When the input is rejected, the command is not executed, and the user may have to resubmit the input. 
   Having described preferred embodiments of a system and method for ensuring robustness in natural language understanding (which are intended to be illustrative and not limiting), it is noted that modifications and variations can be made by persons skilled in the art in light of the above teachings. It is therefore to be understood that changes may be made in the particular embodiments of the invention disclosed which are within the scope and spirit of the invention as outlined by the appended claims. Having thus described the invention with the details and particularity required by the patent laws, what is claimed and desired protected by Letters Patent is set forth in the appended claims.