Temporal Translation Grammar for Language Resources
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Solution Overview
Problem
Existing language translation techniques face difficulties in extending temporal translation resources to new contexts, languages, or dialects, as manual rule sets and machine learning recognizers are implementation-specific, making it hard to share resources and extend them efficiently across different devices and services.
Innovation Solution
A temporal translation grammar is introduced, comprising recognition, normalization, and translation rules, which are implementation-independent and can be used to develop shared resources, facilitating the translation of temporal elements in expressions across various contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual rule sets are developed for a particular context, then translation accuracy for that context is improved, but adaptability to new languages and contexts deteriorates
Solution Approach 1:
The translation system is segmented into three independent functional modules: recognition rules for identifying temporal elements, normalization rules for standardizing temporal expressions, and translation rules for converting to target language. This segmentation allows each module to be developed and optimized independently, improving overall adaptability while maintaining accuracy through specialized rule sets for different languages and contexts.
Solution Approach 2:
The patent creates a universal temporal translation framework that can handle multiple languages and contexts through a single unified grammar system. The same three-rule structure (recognition, normalization, translation) serves all languages, with only the specific rule patterns needing to be adapted. This multi-functional approach improves adaptability without sacrificing the precision achieved through context-specific rule development.
2Reliability
If implementation-specific translation resources are developed, then translation performance for that implementation is improved, but ease of sharing and extending resources deteriorates
Solution Approach 1:
The patent introduces a standardized temporal translation grammar as an intermediary layer between the source temporal expressions and the target language translations. This grammar serves as a universal mediator that can be shared across different implementations, languages, and contexts. Each implementation can leverage the same grammar foundation while adapting specific rules, making resource sharing and extension straightforward while maintaining high translation performance through implementation-optimized rule sets.
3Measurement precision
If complex machine learning recognizers are used, then translation accuracy is improved, but device complexity and computational requirements deteriorate
Solution Approach 1:
The patent replaces complex machine learning recognizers with simple, rule-based pattern matching systems. Instead of using computationally intensive neural networks or complex algorithms, the system employs straightforward regex patterns and logical rules that are easy to implement, modify, and share. These simple rule objects can be quickly generated and updated without requiring significant computational resources, while still achieving accurate temporal translation through carefully designed recognition, normalization, and translation rules.
Data Source
AI summary
In language translation and intent understanding scenarios, the automated translation of expressions including temporal elements (e.g., calendar dates, date ranges, times, and durations) may be achieved by an implementation of translation techniques, such as compiled rule sets and/or machine learning recognizers that have been trained with a training set. However, sharing development resources among various implementations may be difficult; e.g., updates that extend a rule set for application of the translation techniques to a new context may be difficult to utilize while updating a machine learning recognizer. Presented herein are techniques for facilitating the development of temporal translation resources by providing a temporal translation grammar, comprising recognition rules that specify the recognition of temporal elements; normalization rules that specify the normalization of recognized temporal elements into normalized temporal elements and temporal intent; and translation rules that translate the normalized temporal elements of an expression into dates in a translated expression.