Active learning for spoken language understanding
View Patent ↗Disclosed is a system and method of training a spoken language understanding module. Such a module may be utilized in a spoken dialog system. The method of training a spoken language understanding module comprises training acoustic and language models using a small set of transcribed data S t , recognizing utterances in a set S u that are candidates for transcription using the acoustic and language models, computing confidence scores of the utterances, selecting k utterances that have the smallest confidence scores from S u and transcribing them into a new set S i , redefining S t as the union of S t and S i , redefining S u as S u minus S i , and returning to the step of training acoustic and language models if word accuracy has not converged.
1. A non-transitory computer-readable storage medium storing instructions for controlling a computing device to generate a classifier, the instructions comprising:
(1) training a classifier using current training data S t , the training data S t generated by sampling a plurality of utterances;
(2) classifying utterances in a pool S u using the trained classifier;
(3) computing a call type confidence score for each utterance;
(4) sorting candidate utterances with respect to the confidence score of the maximum scoring call type;
(5) selecting the lowest scored k utterances from S u using the confidence scores and labeling them to define a labeled set S i ;
(6) redefining S t =S t ∪S i ; and
(7) redefining S u =S u −S i .
2. The non-transitory computer-readable storage medium of claim 1 , wherein steps 1 through 7 are practiced until labelers and utterances are no longer available.
3. The non-transitory computer-readable storage medium of claim 1 , wherein k is more than one.
4. The non-transitory computer-readable storage medium of claim 1 , wherein selecting k utterances from S u further comprises leaving out utterances with confidence scores indicating that the utterances were correctly recognized.
5. The non-transitory computer-readable storage medium of claim 1 , wherein selecting k utterances from S u further comprises selecting the lowest scoring k utterances from S u .
6. The non-transitory computer-readable storage medium of claim 1 , wherein selecting k utterances from S u further comprises selecting utterances according to a confidence score distribution that is closest to a prior distribution.
7. A non-transitory computer-readable storage medium storing instructions for controlling a computing device to generate a spoken language understanding module, the instructions comprising, from a small amount of training data S t and a larger amount of unlabeled data S u :
(1) training a plurality of classifiers independently using a training data set S t , the training data S t generated by sampling a plurality of utterances;
(2) classifying utterances in a set S u using the plurality of classifiers and computing a call type confidence score for all utterances;
(3) sorting candidate utterances with respect to a score of the maximum scoring call type according to one of the classifiers if the classifiers disagree;
(4) selecting and labeling the lowest scored k utterances from S u to define a labeled set S i and redefining S t and S u as follows:
(5) S t =S t ∪S i ; and
(6) S u =S u −S i , wherein the labeled utterances are used to generate the spoken language understanding module.
8. The non-transitory computer-readable storage medium of claim 7 , wherein the steps occur only while labelers and utterances are available.
9. The non-transitory computer-readable storage medium of claim 7 , wherein selecting k utterances from S u further comprises selecting utterances according to a confidence score distribution that is closest to a prior distribution.
10. The non-transitory computer-readable storage medium of claim 7 , wherein selecting k utterances from S u further comprises selecting the lowest scoring k utterances from S u .
11. A method of generating a spoken dialog understanding module, the method causing a processor of a computing device to perform steps comprising, from a small amount of training data S t and a larger amount of unlabeled data S u :
classifying via the processor of the computing device utterances in an unlabelled data set S u using a plurality of classifiers;
computing via the processor of the computing device a call type confidence score for all utterances;
selecting utterances for labeling from the unlabeled data S u based on whether the classification from the plurality of classifiers disagree;
redefining S t =S t ∪a labeled set S i ;
redefining S u =S u −S i
labeling the selected utterances; and
generating a spoken language understanding module using the labeled utterances.
12. The method of claim 11 , wherein the selected utterances are the lowest scored k utterances from S u to the final label set S i , wherein the method further causes the processor of the computing device to perform steps comprising redefining S t and S u as follows:
S t =S t ÅS i ; and
S u =S u −S i , wherein the labeled utterances are used to generate the spoken language understanding module.
13. The method of claim 12 , wherein the steps occur only while labelers and utterances are available.
14. The method of claim 12 , wherein selecting k utterances from S u further comprises selecting utterances according to a confidence score distribution that is closest to a prior distribution.
15. The method of claim 12 , wherein selecting k utterances from S u further comprises selecting the lowest scoring k utterances from S u .