Search Query Interpretation Evaluation Using Temporal and Cluster Features
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Solution Overview
Problem
Existing systems struggle to efficiently evaluate the quality of human interpretations for natural language search queries, especially when scaled across multiple languages, leading to inefficiencies and increased costs due to the reliance on manual human evaluation and the inability to analyze interaction signals.
Innovation Solution
A predictive method using a pre-trained NLP model, such as BERT, fine-tuned with search query datasets, generates a final model like logistic regression to assess the accuracy of human interpretations, incorporating clustering and temporal features to reduce manual classification needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual human evaluation is used to rate the quality of search query interpretations, then evaluation accuracy is improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent creates a predictive model that copies the evaluation capability of human annotators. The model is trained on human-labeled data and then replicates human evaluation patterns automatically, replacing the need for continuous manual evaluation while maintaining similar accuracy levels.
Solution Approach 2:
The patent replaces the mechanical process of manual human evaluation with an automated computational system. The predictive model uses machine learning algorithms to substitute the human annotation process, eliminating time consumption and cost associated with manual evaluation.
2Measurement precision
If manual human evaluation is used for search query interpretations, then evaluation quality is improved, but scalability across multiple languages deteriorates
Solution Approach 1:
The predictive model is designed to be universal across multiple languages and domains. Once trained on multilingual data, the same model can evaluate search query interpretations in various languages without requiring separate human evaluation teams for each language, thus achieving scalability while maintaining evaluation quality.
Solution Approach 2:
The model copies human evaluation capabilities across different languages by learning from multilingual training data. This allows the system to replicate human-quality evaluation for any supported language automatically, eliminating the need to scale human resources proportionally with language diversity.
3Productivity
If interaction signals are analyzed to evaluate search query quality, then evaluation efficiency is improved, but the ability to capture nuanced user feedback deteriorates
Solution Approach 1:
The predictive model acts as an intermediary that processes interaction signals and transforms them into meaningful quality evaluations. The model learns to interpret nuanced user behaviors from training data, capturing subtle feedback patterns that raw interaction signals alone might miss, while maintaining high evaluation efficiency.
Data Source
AI summary
A method for evaluating an interpretation for a search query is disclosed. The method includes receiving, by one or more processing circuits, a training dataset. The method includes training a first model utilizing the training dataset. The first model is trained to evaluate whether a human interpretation for a search query is correct. The method includes receiving a search query and a human interpretation for the search query. The method includes determining an initial evaluation of whether the human interpretation for the search query is correct utilizing the first model. The method includes generating a second model utilizing the initial evaluation from the first model, temporal features relating to the search query, and cluster features relating to the search query, and determining a final evaluation of whether the human interpretation for the search query is correct utilizing the second model.