Semantic Parser Embedding Hotfixes for Triggering Errors
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Solution Overview
Problem
Existing semantic parser models face challenges with over-triggering and under-triggering, requiring extensive retraining which is computationally intensive and time-consuming, and they struggle to handle queries with varying argument structures without specific training for different applications.
Innovation Solution
A semantic parser model that generates structured data output by processing natural language queries using embedding space representations, allowing developers to provide example queries to hotfix over-triggering or under-triggering without full retraining, and supports compositional queries with sub-intents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing semantic parser models are retrained to handle over-triggering or under-triggering issues, then model accuracy improves, but computational resources and time are excessively consumed
Solution Approach 1:
The patent segments the model adjustment process into two distinct approaches: (1) full retraining for comprehensive model updates, and (2) embedding space modification for targeted, rapid fixes. This segmentation allows developers to choose the appropriate granularity based on the severity and scope of triggering issues, avoiding unnecessary full retraining cycles while maintaining model accuracy.
Solution Approach 2:
The patent implements preliminary action by providing developers with the capability to modify embedding spaces directly before deployment. By allowing pre-adjustment of embedding representations based on expected query patterns and triggering behaviors, the system can proactively address potential over-triggering or under-triggering issues without requiring post-deployment retraining, thus saving computational resources and time.
2Reliability
If existing semantic parser models are retrained to handle over-triggering or under-triggering issues, then model accuracy improves, but computational resources are excessively consumed
Solution Approach 1:
The patent segments the model adjustment process into two distinct approaches: (1) full retraining for comprehensive model updates, and (2) embedding space modification for targeted, rapid fixes. This segmentation allows developers to choose the appropriate granularity based on the severity and scope of triggering issues, avoiding unnecessary full retraining cycles while maintaining model accuracy.
Solution Approach 2:
The patent applies parameter changes by modifying the embedding space representations directly, which are key parameters in the semantic parsing process. By adjusting embedding vectors and dimensions to better represent intended meanings and reduce triggering issues, the system achieves model improvement through parameter optimization rather than computationally intensive full retraining, significantly reducing energy consumption.
3Adaptability or versatility
If a single semantic parser model is designed to handle multiple applications with varying argument structures, then model versatility improves, but the model requires extensive training for each application
Solution Approach 1:
The patent implements universality by creating a single semantic parser model with a unified embedding space that can handle multiple applications and query types. The embedding space is designed to be application-agnostic, representing semantic meanings in a way that transcends specific argument structures. Developers can deploy this universal model across different applications and adjust the embedding space to accommodate varying requirements, eliminating the need for separate trained models for each application.
Solution Approach 2:
The patent implements preliminary action by providing developers with the capability to modify embedding spaces directly before deployment. By allowing pre-adjustment of embedding representations based on expected query patterns and triggering behaviors, the system can proactively address potential over-triggering or under-triggering issues without requiring post-deployment retraining, thus saving computational resources and time.
4Adaptability or versatility
If a single semantic parser model is designed to handle multiple applications with varying argument structures, then model versatility improves, but extensive training data is required
Solution Approach 1:
The patent implements universality by creating a single semantic parser model with a unified embedding space that can handle multiple applications and query types. The embedding space is designed to be application-agnostic, representing semantic meanings in a way that transcends specific argument structures. Developers can deploy this universal model across different applications and adjust the embedding space to accommodate varying requirements, eliminating the need for separate trained models for each application.
Solution Approach 2:
The patent applies parameter changes by modifying the embedding space representations directly, which are key parameters in the semantic parsing process. By adjusting embedding vectors and dimensions to better represent intended meanings and reduce triggering issues, the system achieves model improvement through parameter optimization rather than computationally intensive full retraining, significantly reducing energy consumption.
Data Source
AI summary
Techniques disclosed herein are directed towards generating structured data output based on processing a natural language user query using a semantic parser model. Many implementations include identifying one or more argument spans in the given natural language user query based on comparing an embedding space representation of a candidate argument with an embedding space representation of an example query, where the example query is provided by a developer. Various implementations include hotfixing an under-triggering model and/or an over-triggering model based on additional or alternative example queries provided by a developer.