Intent Classification Training Using Dependent Intent Descriptions
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Solution Overview
Problem
Existing intent classification models struggle to accurately map user utterances to predefined intents, necessitating improved methods for training and enhancing the accuracy of intent classification in task-oriented dialogue systems.
Innovation Solution
A method and system for training an intent classification model using intent descriptions, including independent, dependent, and refined intent descriptions, with a ranker to calculate similarity and adjust the number of intent options, and a retraining process to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional intent classification models are used without intent descriptions, then the system complexity is lower, but the intent classification accuracy is insufficient
Solution Approach 1:
The patent applies preliminary action by pre-defining intent descriptions for various user intents before the classification process. These intent descriptions serve as prepared reference patterns that guide the classification model, allowing it to accurately match user utterances against known intent patterns without requiring complex real-time analysis.
Solution Approach 2:
The patent introduces intent descriptions as an intermediary element between user utterances and the classification decision. These descriptions act as a mediating layer that translates raw user input into structured intent categories, improving classification accuracy while maintaining manageable system complexity through the use of predefined textual patterns.
2Adaptability or versatility
If more intent options are provided to the classification model, then the coverage of user intents is improved, but the difficulty of selecting the correct intent increases
Solution Approach 1:
The patent applies local quality by providing specific, tailored intent descriptions for each intent category rather than using generic classifications. Each intent has its own distinctive description that highlights its unique characteristics, making it easier for the model to differentiate between similar intents and select the correct one even when multiple intents are available.
Solution Approach 2:
The patent segments the intent space into distinct categories, each with its own dedicated description. This segmentation allows the classification system to handle multiple intents independently and systematically, reducing the confusion that would arise from treating all intents as a single undifferentiated group.
3Measurement precision
If intent descriptions are added as input features, then the classification accuracy is improved, but the input data processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-defining and storing intent descriptions in a accessible format before classification occurs. This preparation allows the system to quickly retrieve and compare descriptions during the classification process, minimizing the time penalty that would otherwise result from generating or analyzing descriptions in real-time.
Data Source
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AI summary
The system for training an intent classification model using intent descriptions of the present disclosure may generates a dataset including intent descriptions of the intent classification model from a language model, perform intent classification by using at least a portion of the dataset as an input prompt for the intent classification mode, and determine performance of the intent classification model by using at least a portion of the dataset as an input prompt for the intent classification model, wherein the dataset may include first data including an independent intent description written independently from other intent descriptions and second data including a dependent intent description written dependently on other intent descriptions.