Multilingual NLP System for Intent Binding and API Generation
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Solution Overview
Problem
Conventional natural language processing systems are limited to the English language and lack the capability to bind different language sets, making it difficult for non-English speaking users to interact with them effectively.
Innovation Solution
A natural language processing system that includes a model trainer, an action manager, and a processor to determine the language of a natural language query and bind its intent to an appropriate action, using a training corpus in multiple languages to generate API requests, enabling interaction in languages other than English.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the natural language processing system is designed to support multiple languages, then the accessibility and usability for a broader population is improved, but the device complexity increases due to the need for multi-lingual training corpora and language detection mechanisms
Solution Approach 1:
The system employs a single unified natural language processing model that has been trained on multi-lingual training corpora to handle multiple languages. This universal model approach allows the system to support multiple languages without requiring separate specialized models for each language, thereby reducing the complexity that would otherwise arise from maintaining multiple language-specific processing systems.
Solution Approach 2:
The system introduces a language detection mechanism as an intermediary component that automatically identifies the language of incoming queries and routes them to the appropriate processing pathway within the unified model. This intermediary layer enables multi-lingual support while maintaining a single core processing architecture, thus managing system complexity.
2Ease of operation
If the system uses a unified multi-lingual model, then the ease of operation is improved for users, but the manufacturing precision of language-specific bindings may be compromised
Solution Approach 1:
The system performs preliminary language detection and intent classification before executing specific actions. By pre-identifying the language and binding intents to appropriate actions in advance during the processing pipeline, the system ensures accurate language-specific bindings are established before action execution, maintaining precision while preserving ease of operation.
Solution Approach 2:
The processing pipeline is segmented into distinct stages: language detection, intent binding, and action execution. This segmentation allows each stage to be optimized independently, ensuring that language-specific bindings are established with high precision in the intent binding stage while the user interacts through simple natural language in the ease of operation stage.
3Productivity
If the system processes queries in real-time, then the productivity is improved, but the loss of time for language detection and intent binding increases
Solution Approach 1:
The system performs language detection and intent binding as preliminary actions within the real-time processing pipeline. By pre-processing these tasks before action execution, the system minimizes the time loss associated with language detection while maintaining overall real-time productivity. The preliminary action is integrated into the processing flow rather than being a separate bottleneck.
Solution Approach 2:
The system maintains continuous processing by executing language detection, intent binding, and action execution in an uninterrupted sequential flow. The preliminary actions are performed continuously as queries arrive, ensuring that no significant time is lost to language detection while preserving real-time productivity through continuous useful action.
Data Source
AI summary
A natural language processing system includes a model trainer operable to train a natural language processing model based on a training corpus, wherein the training corpus includes a data set from one of multiple languages. An action manager curates application programming interfaces, and a processor receives a natural language query at the natural language processing system, wherein the natural language query is directed to a remote component, and the natural language query is in a language other than English. The processor determines the language of the natural language query, and binds the intent of the natural language query to an action, wherein the action is a function to be carried out by the remote component.