Semantic Parser Parameter Optimization for Natural Language Inference
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Solution Overview
Problem
Existing systems face challenges in presenting appropriate inference results for natural language inputs due to the difficulty in converting user queries from natural language to formal language effectively, especially when using knowledge bases.
Innovation Solution
An information processing apparatus and method that utilizes a pre-trained machine learning model to convert natural language texts into formal language queries, applies deductive or abduction inference methods to a knowledge base, and updates the model to optimize parameter settings for accurate inference results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning is used to automatically construct a semantic parser, then the ability to convert natural language to formal language is improved, but the accuracy of inference results for natural language inputs deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where inference results are used to update the semantic parser parameters. The system compares generated queries with actual inference results and uses this feedback to refine the conversion model, thereby improving accuracy over time while maintaining adaptability to various natural language inputs
Solution Approach 2:
The patent dynamically adjusts parameters of the semantic parser based on the relationship between generated queries and actual inference results. By changing parameters such as weight coefficients in the loss function and updating model weights through backpropagation, the system optimizes the conversion accuracy while preserving its ability to handle diverse natural language expressions
2Measurement precision
If manual rules are constructed for converting natural language to formal language, then the precision of query generation is improved, but the complexity of rule construction and maintenance deteriorates
Solution Approach 1:
The patent replaces manual rule-based query generation with an automated machine learning system. Instead of requiring experts to hand-craft conversion rules, the system uses neural networks trained on data to automatically perform the natural language to formal language conversion, eliminating the complexity of rule maintenance while achieving high precision
Solution Approach 2:
The system performs self-improvement by automatically learning from data and adjusting its own parameters. The semantic parser trains on examples of natural language queries and their corresponding formal language representations, enabling it to improve its conversion accuracy without external intervention or manual rule updates
3Speed
If a semantic parser converts natural language to formal language queries, then the speed of question answering is improved, but the accuracy of understanding semantic structure deteriorates
Solution Approach 1:
The patent maintains continuous improvement of the semantic parser through ongoing training and parameter updates. The system continuously refines its understanding of semantic structures by learning from new data and feedback, ensuring that both speed and accuracy improve over time rather than trading one for the other
Data Source
AI summary
An information processing apparatus 10 includes an acceptance unit 12, a formal language generation unit 14, an inference unit 16, and an update unit 18. The formal language generation unit 14 generates training expressions in a formal language based on parameters prepared in advance and pieces of text accepted by the acceptance unit 12. The inference unit 16 executes at least one inference out of deductive inference and abduction on the above training expressions using a knowledge base prepared in advance. The update unit 18 compares an inference result of the inference unit 16 with a model answer input in advance, and updates parameters such that an inference result that matches the model answer is likely to be obtained through inference performed by the inference unit 16.