Grammatically Diverse Test Set for Deep Question Answering
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Solution Overview
Problem
Deep question answering systems require a diverse training set of natural language sentences to avoid overfitting, but existing methods fail to provide adequate grammatical diversity, leading to poor predictive performance due to syntactic similarity among training sentences.
Innovation Solution
A method for automatically classifying natural language sentences based on their grammatical structure using syntactic and semantic relationships, generating a full parse tree and traversing it to record traversal steps as a classification, allowing for the creation of a diverse test set tailored to a specific element of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing methods are used to create training sets, then the process is simple, but the grammatical diversity is insufficient leading to poor predictive performance
Solution Approach 1:
The patent segments the sentence analysis process into distinct syntactic categories (noun phrases, verb phrases, prepositional phrases, etc.) and analyzes each segment's grammatical structure separately. This segmentation enables comprehensive grammatical diversity assessment while maintaining systematic organization, resolving the contradiction between thorough analysis and process complexity.
Solution Approach 2:
The patent performs preliminary syntactic classification and parse tree generation before model training. By pre-processing sentences to extract and categorize grammatical structures in advance, the system ensures grammatical diversity is built into the training set before deployment, improving predictive performance without adding complexity during the actual training process.
2Adaptability or versatility
If a diverse training set is created using full syntactic analysis, then grammatical diversity is improved, but the processing complexity and time increase
Solution Approach 1:
The patent applies different levels of syntactic analysis to different sentences based on their specific characteristics and the model's needs. Rather than uniformly analyzing every sentence with full parse trees, the system selectively applies analysis depth, achieving grammatical diversity while reducing unnecessary processing time for sentences that don't require exhaustive analysis.
Solution Approach 2:
The system dynamically adjusts analysis parameters such as parse tree depth, syntactic category granularity, and classification thresholds based on the specific training requirements. This parameter optimization allows the system to achieve adequate grammatical diversity with reduced processing time by avoiding over-analysis of sentences that don't contribute significantly to diversity.
3Productivity
If simple classification methods are used, then the process is faster, but the syntactic similarity among training sentences increases leading to overfitting
Solution Approach 1:
The patent implements a tiered classification approach where essential syntactic features are analyzed for all sentences (partial action to maintain speed), while additional detailed syntactic analysis is applied selectively to sentences that benefit most from it (excessive action where needed). This ensures adequate syntactic diversity without the full overhead of comprehensive analysis on every sentence, balancing speed and generalization.
Data Source
AI summary
A grammatically diverse test set of natural language sentences for a deep question answering system is provided by analyzing a given sentence to characterize its syntactical classification, and adding the sentence to the test set if its classification is sufficiently different from other sentences already in the test set. A particular sentence may be selected for inclusion according to a desired syntactic distribution. Multiple sentences having the exact same classification may be allowed subject to a maximum number of such sentences. The test set is adapted to an element of interest by characterizing each syntactical classification relative to the element of interest. The analysis derives a parse tree, identifies a particular node of the tree corresponding to the element of interest, and extracts syntactic information by traversing the tree starting at the particular node and ending at the root node of the tree according to different traversal schemes.