Context Tag Ground Truth for NLU Disambiguation
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Solution Overview
Problem
Conventional natural language classification systems face challenges in disambiguating intents associated with the same utterances, particularly when the context changes, as they rely on rules engines that require updates and cannot accurately classify utterances in new environments without migration.
Innovation Solution
A method that involves obtaining user input, collecting context data, generating context tags, and updating ground truth data to associate utterances with semantically consistent intents, allowing for accurate classification across different contexts without the need for a rules engine, using a system that includes a natural language classification engine and a ground truth contextualizer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rules engines are used to resolve disambiguation with intents, then classification accuracy can be maintained in known environments, but the system requires updates and cannot accurately classify utterances in new environments without migration
Solution Approach 1:
The system performs self-service by automatically generating context tags and updating ground truth data without requiring manual rules engine updates. The ground truth contextualizer autonomously processes utterances, generates context tags based on collected context data, and updates training data, enabling the system to adapt to new environments independently
Solution Approach 2:
The system performs preliminary action by pre-collecting context data and pre-generating context tags before classification is needed. The ground truth contextualizer prepares updated training data with context tags in advance, so when new utterances arrive in new environments, the classification can proceed immediately without waiting for rules engine updates
2Adaptability or versatility
If context data collection and context tag generation are implemented, then adaptability to new environments improves, but system complexity increases
Solution Approach 1:
The ground truth contextualizer serves multiple functions: it collects context data, generates context tags, selects ground truth, and updates training data. This multi-functional component reduces overall system complexity by consolidating what would otherwise require separate modules for each task
Solution Approach 2:
Context tags serve as an intermediary between raw context data and the classification process. Instead of directly processing complex context data during classification, the system uses simplified context tags as mediators that capture essential contextual information, reducing computational complexity during the actual classification operation
3Productivity
If ground truth data is updated by attaching context tags, then classification efficiency in evolving domains improves, but data processing time increases
Solution Approach 1:
The system performs preliminary action by generating context tags and updating ground truth data in advance during off-peak times or asynchronously. This allows the computationally intensive data processing to occur before it is critically needed, so that during actual classification operations, the system can quickly retrieve pre-processed data without time pressure
Data Source
AI summary
Methods, computer program products, and systems are presented. The methods include, for instance: obtaining an utterance input from a user agent, and collecting context data of the utterance input. A context tag is generated based on the context data, and one or more ground truth having respective utterance semantically identical to the utterance input is selected. Semantical relationship between the context tag and an intent of the selected ground truth is examined and the selected ground truth is updated with the context tag.