Natural Language Model Training with Binary Query Classification
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Solution Overview
Problem
Voice recognition systems, particularly in natural language understanding (NLU) systems, struggle with accurately recognizing speech and text queries when they include obsequious expressions, leading to reduced match scores and increased complexity in model training, while removing these expressions can result in poor search results.
Innovation Solution
A natural language model-based system that minimally trains and adapts for legacy systems, using a classifier binary model to distinguish between queries with and without obsequious expressions, facilitating pre-processing assistance for NLU systems to maintain accuracy and reduce training complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the natural language model is trained to recognize queries with obsequious expressions, then the system can identify polite speech patterns, but the match scores of previously trained speeches are reduced and additional costly and lengthy training is required
Solution Approach 1:
The system segments the query processing into two independent components: a binary classifier that detects the presence of obsequious expressions, and the existing NLU pipeline that processes the query. This segmentation allows the system to identify polite speech patterns without retraining the entire NLU model, thereby preserving match scores of previously trained speeches while gaining the ability to recognize obsequious expressions.
Solution Approach 2:
A binary classifier acts as an intermediary component between the user query and the NLU system. This intermediary detects whether obsequious expressions are present and passes the query to the NLU system accordingly, without requiring the NLU model itself to be retrained. This intermediary approach maintains the reliability of existing speech matches while adding the capability to recognize polite speech patterns.
2Adaptability or versatility
If the natural language model is trained to include obsequious expressions in queries, then the system can process polite commands, but the system architecture becomes unnecessarily complicated
Solution Approach 1:
The system architecture is segmented into a simple binary classifier component and the existing NLU system. The binary classifier is a standalone, lightweight module that adds polite command processing capability without complicating the core NLU architecture. This segmentation allows the system to process polite commands while maintaining architectural simplicity.
Solution Approach 2:
The binary classifier performs preliminary detection of obsequious expressions before the query is processed by the NLU system. This preliminary action allows the system to handle polite commands by simply identifying the presence of polite markers, without requiring complex architectural changes to the main processing pipeline.
3Device complexity
If obsequious expressions are removed from search queries, then the NLU system can process queries more simply, but content search applications with entity titles that include such expressions yield poor results
Solution Approach 1:
Instead of completely removing obsequious expressions from all queries, the system applies partial action by selectively handling them. The binary classifier identifies queries with obsequious expressions and processes them appropriately, while leaving other queries unchanged. This partial approach maintains search result accuracy for entities with titles containing polite expressions while keeping the processing system relatively simple.
Solution Approach 2:
The system changes the parameter of query processing by introducing a detection mechanism that identifies the presence of obsequious expressions. Rather than uniformly removing or keeping such expressions, the system adjusts its processing behavior based on the detected parameter state, thereby maintaining both processing simplicity and search accuracy.
Data Source
AI summary
Systems and methods for determining to perform an action of a query using a trained natural language model of a natural language understanding (NLU) system are disclosed herein. A text string corresponding to a prescribed action includes at least a content entity is received. A determination is made as to whether the text string corresponds to an audio input of a first group. In response to determining the text string corresponds to an audio input of a first group, a determination is made as to whether the text string includes an obsequious expression. In response to determining the text string corresponds to an audio input of a first group and in response to determining the text string includes an obsequious expression, a determination is made to perform the prescribed action. In response to determining the text string corresponds to an audio input of a first group and in response to determining the text string does not include the obsequious expression, a determination is made to not perform the prescribed action.