Zero-Shot Intent Recognition Model for Dialog Systems
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Solution Overview
Problem
Current dialog systems face challenges in developing high-performance natural language understanding (NLU) components due to lengthy interactions between domain and dialog experts, frequent updates in intent labels, and high costs associated with data annotation and model retraining.
Innovation Solution
The system employs a novel task definition format called intent flow, which allows domain experts to unambiguously describe expected task logic, and a Zero-Shot Intent Recognition (ZSIR) Model that generates paraphrase tasks to create training datasets without requiring pre-existing dialog data, enabling efficient intent recognition and handling of frequent updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional intent recognition models are used with frequent updates to intent labels, then the system can adapt to changing requirements, but the cost and time for data re-annotation and model retraining increase significantly
Solution Approach 1:
The patent performs preliminary actions by pre-defining intent labels and their hierarchical relationships before actual dialog data arrives. The system pre-processes and structures intent definitions in advance, creating a ready-to-use framework that can quickly accommodate new intents without requiring complete re-annotation of dialog data. This preliminary structuring of intent space enables rapid adaptation when new intent labels are introduced.
Solution Approach 2:
The patent uses copying by leveraging the hierarchical structure and relationships between parent and child intents. When a new intent label is introduced, the system can copy and adapt existing intent structures, templates, and annotation schemas rather than creating everything from scratch. This copying approach maintains consistency across intent updates while significantly reducing the effort required for re-annotation and retraining.
2Manufacturing precision
If domain experts and dialog experts engage in lengthy interactions to define dialog task behavior, then the system can achieve proper task definition, but the development process becomes slow and inefficient
Solution Approach 1:
The patent introduces an intermediary layer of structured intent definitions and hierarchical intent structures that mediate between domain experts' requirements and dialog experts' implementation. This intermediary framework translates vague domain requirements into precise, structured intent specifications automatically, reducing the need for lengthy back-and-forth interactions while maintaining high precision in task definition.
Solution Approach 2:
The patent applies parameter changes by transforming the representation of task definitions from unstructured natural language descriptions into structured parameters including intent labels, hierarchical relationships, and associated dialog flows. This parameterization enables automated processing and reduces manual iteration, significantly improving development speed while preserving the precision needed for accurate task definition.
3Device complexity
If a finite list of intent labels is used, then the system can operate with manageable complexity, but the system cannot recognize unlimited possible types of user intentions
Solution Approach 1:
The patent applies segmentation by dividing the space of user intentions into a hierarchical structure of parent intents and child intents. This segmentation allows the system to manage a finite set of labeled categories while capturing the diversity of user intentions through hierarchical relationships. The segmented structure enables the system to handle unlimited variations of user intent by organizing them into manageable hierarchical groups.
Solution Approach 2:
The patent adds another dimension to intent representation by introducing hierarchical levels (parent-child relationships) and structural relationships between intents. This dimensional expansion allows the system to represent unlimited user intentions within a finite labeled framework by organizing intents across multiple hierarchical dimensions, thereby increasing coverage without proportionally increasing management complexity.
Data Source
AI summary
A system and method of creating the natural language understanding component of a speech/text dialog system. The method involves a first step of defining user intent in the form of an intent flow graph. Next, (context, intent) pairs are created from each of the plurality of intent flow graphs and stored in a training database. A paraphrase task is then generated from each (context, intent) pair and also stored in the training database. A zero-shot intent recognition model is trained using the plurality of (context, intent) pairs in the training database to recognize user intents from the plurality of paraphrase tasks in the training database. Once trained, the zero-shot intent recognition model is applied to user queries to generate semantic outputs.