Multilingual Classification System via Repository Translation
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Solution Overview
Problem
Companies face significant challenges in generating and maintaining classification systems for contact centers that support multiple languages, as each language requires a unique system, especially when dealing with a large number of potential questions and answers, leading to a need for efficient translation methods.
Innovation Solution
The method involves using a mechanical translation process to translate a sample response repository into target languages, followed by applying natural language understanding processes to create a classification system that can classify communications, including speech recognition and rule-based systems, enabling the generation of multi-lingual classification systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a separate classification system is generated for each language, then the system can accurately classify communications in that specific language, but the complexity and cost of maintaining multiple systems increases significantly
Solution Approach 1:
The patent creates a universal classification system that can handle multiple languages through translation. Instead of building separate classification systems for each language, the system translates the sample response repository into target languages and reuses the same classification logic, making the system multi-functional across languages while maintaining accuracy.
Solution Approach 2:
The patent uses mechanical translation to create copies of the sample response repository in target languages. The translated repository serves as a template that can be reused with the same classification system, avoiding the need to manually create separate classification systems for each language while preserving classification accuracy.
2Reliability
If a separate classification system is generated for each language, then the system can handle language-specific nuances, but the time and resources required for system generation and maintenance increase
Solution Approach 1:
The patent performs preliminary translation of the sample response repository into target languages before the classification system needs to be deployed. This advance preparation allows the system to handle language-specific nuances immediately upon deployment without requiring time-consuming manual translation and system generation for each language.
Solution Approach 2:
The system uses automated mechanical translation processes to generate translated sample response repositories, eliminating the need for manual translation work. This self-service approach significantly reduces the time and human resources required to prepare classification systems for multiple languages while maintaining the ability to handle language-specific nuances.
3Measurement precision
If manual translation and adaptation of classification systems is performed for each language, then the classification accuracy is maintained, but the cost and complexity of the process increases
Solution Approach 1:
The patent replaces manual translation and adaptation processes with automated mechanical translation systems. This substitution maintains classification accuracy by preserving the structure and semantics of the sample response repository while dramatically simplifying the system generation process, making it easier to deploy multi-lingual classification systems without manual intervention.
Data Source
AI summary
A method and apparatus are provided for translating a classification system in a source language into one or more additional target languages. The classification system employs a sample response repository to learn to classify a communication into one of a plurality of predefined categories. The sample response repository comprises a plurality of prior communications each having a classification. The present invention translates the sample response repository using a mechanical translation process to generate a translated response repository. A natural language understanding process is then applied to the translated response repository to generate a natural language understanding module that can classify a communication in the target language. The natural language understanding process can employ statistical methods or a rule-base of classification rules that determine how communications are classified. A speech recognition statistical model compilation process can optionally be applied to the translated target language response repository to generate a speech recognition module in the target language.