Single NLP Model for Multilingual Intent Recognition via Intermediate Vectors
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Solution Overview
Problem
Current Natural Language Processing (NLP) models face challenges in accurately processing user inputs in multiple languages, particularly with poorly-structured or poorly-worded texts, and require extensive training data and resources for each language, making it difficult to understand user intent across different dialects and grammatical structures.
Innovation Solution
A Single Natural Language Processing (SNLP) model translates user inputs into an intermediate language, generates intermediate input vectors, and processes them using predefined mechanisms to identify responses, which are then translated back, employing an elastic stretching mechanism to improve intent mapping and reduce the need for multiple language-specific systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple language-specific NLP models and training data are used for each language, then language coverage and intent recognition accuracy are improved, but system complexity and resource requirements increase significantly
Solution Approach 1:
The patent applies universality by creating a single NLP model that can process multiple languages through code-switched training data, where the model learns to handle mixed language inputs (e.g., Hindi-English code-switched text) and map them to a unified intent space, eliminating the need for separate language-specific models
Solution Approach 2:
The patent merges multiple language processing capabilities into a single unified model by combining code-switched training data from different languages, allowing the model to learn cross-lingual patterns and process multilingual inputs through one integrated system rather than multiple separate systems
2Measurement precision
If extensive training data is collected and tagged for each language, then intent recognition accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The patent changes the parameter of training data representation by using code-switched text (mixing languages) instead of monolingual training data, which allows the model to learn from more diverse and naturally occurring language patterns while reducing the need for extensive separate training corpora for each language
Solution Approach 2:
The patent creates composite training data by combining multiple languages into code-switched training corpora, where Hindi, English, and other languages are mixed in natural conversational patterns, enabling the model to learn cross-lingual intent recognition from a single composite dataset
3Measurement precision
If traditional NLP approaches with separate grammar rules for each language are used, then language-specific accuracy is improved, but ease of operation and maintenance deteriorate
Solution Approach 1:
The patent applies universality by replacing multiple language-specific grammar systems with a single unified NLP model that handles multiple languages through code-switched training, eliminating the need to maintain separate grammar rules and processing pipelines for each language
Data Source
AI summary
The disclosure relates to system and method for processing multilingual user inputs using a Single Natural Language Processing (SNLP) model. The method includes receiving a user input in a source language and translating the user input to generate a plurality of translated user inputs in an intermediate language. The method includes using the SNLP model configured only using the intermediate language to generate a plurality of sets of intermediate input vectors in the intermediate language. The method includes processing, via an Application Programming Interface (API), the plurality of sets of intermediate input vectors using a predefined mechanism. The API is associated with a domain from a plurality of domains. Further, the method includes retrieving a predetermined response from the API based on processing the plurality of sets of intermediate input vectors. The method includes translating the predetermined response to generate a translated response that is rendered to the user.