Label Scorer for Extendable Linguistic Input Recognition
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Solution Overview
Problem
Existing systems for automated interpretation of linguistic input struggle to effectively introduce new labels without additional training examples, especially when only a limited number of examples are available, which is a challenge in scalable label recognition and intent recognition in speech utterance user input.
Innovation Solution
The implementation of a label scorer that uses a combination of sentence encoders and label encoders, along with a similarity scorer, to generalize and map new attribute vectors to appropriate encoding vectors, allowing for the recognition of new labels without requiring extensive retraining, by utilizing parameterized encoders such as feed-forward artificial neural networks and linear transformations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional classifier configuration is used with fixed training examples, then classification accuracy is maintained, but the system cannot effectively recognize new labels without extensive retraining
Solution Approach 1:
The patent segments the classification task into two independent components: (1) a sentence encoder that transforms linguistic input into fixed-length vectors, and (2) a label scorer that evaluates similarity between input vectors and label vectors. This segmentation allows the label scorer to be extended to new labels without retraining the sentence encoder, enabling rapid adaptation to new classification categories.
Solution Approach 2:
The patent introduces fixed label vectors as intermediaries between the sentence encoder and the classification decision. These label vectors serve as static reference points that can be added without modifying the trained sentence encoder, allowing the system to recognize new labels by simply adding new label vectors to the label scorer.
2Adaptability or versatility
If the system is extended to include new labels, then label recognition versatility improves, but system complexity increases
Solution Approach 1:
By separating the sentence encoding function from the label scoring function, the patent enables independent extension of the label set. The sentence encoder remains a fixed, trained component, while the label scorer can accommodate any number of labels through simple vector additions, reducing system complexity despite increased versatility.
Solution Approach 2:
The label scorer is designed as a universal component that can handle any number of labels through a consistent similarity evaluation mechanism. The same scoring function and vector comparison logic apply regardless of the number of labels, allowing the system to scale from a few to many labels without increasing operational complexity.
3Measurement precision
If traditional retraining approaches are used for new labels, then classification accuracy is maintained, but additional training examples are required
Solution Approach 1:
The patent performs preliminary action by pre-training the sentence encoder on a fixed dataset to learn robust sentence representations. This pre-trained encoder then serves as a fixed feature extractor that can be applied to any label set without further training, eliminating the need for additional training examples when adding new labels while maintaining classification accuracy.
Solution Approach 2:
The patent creates a fixed copy of the sentence encoder's transformation capability that can be reused across different label sets. The encoder's learned representation space is copied and applied to evaluate similarity with different label vectors, allowing accurate classification of new labels without copying or adapting training data.
Data Source
AI summary
An approach to extending the recognizable labels of a label recognizer makes use of an encoding of linguistic inputs and label attributes into comparable vectors. The encodings may be determined with artificial neural networks (ANNs) that are jointly trained, and a comparison between the encoding of a sentence input and the encoding of an intent attribute vector may use a fixed function, which does not have to be trained. The encoding of label attributes can generalize permitting adding of a new label via corresponding attributes, thereby avoiding the need to immediately retrain a label recognizer with example inputs.