Multilingual Intent Prediction via Language-Agnostic Embeddings
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Solution Overview
Problem
Existing chatbot systems face challenges in efficiently processing customer inquiries in multiple languages, as they require separate training data sets and machine learning systems for each language, leading to increased costs and complexity.
Innovation Solution
The implementation of a multilingual intent classification system that uses a language-agnostic model pretrained on translation tasks and fine-tuned on chatbot domain data, in conjunction with a unilingual model trained on intent inference tasks, to predict user intents across multiple languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate training data sets and machine learning systems are used for each language, then language-specific intent prediction accuracy is improved, but system complexity and cost increase
Solution Approach 1:
The patent implements a universal machine learning model that can process multiple languages simultaneously. The model is trained on multilingual training data containing user inputs and intent labels in various languages, enabling it to perform intent prediction across different languages without requiring separate language-specific systems. This universal approach maintains prediction accuracy while significantly reducing system complexity and cost.
Solution Approach 2:
The patent combines multiple language processing capabilities into a single integrated machine learning model. Instead of maintaining separate models for each language, the system merges language processing and intent prediction into one unified model that handles multiple languages concurrently, thereby reducing overall system complexity while preserving accuracy.
2Measurement precision
If separate training data sets and machine learning systems are used for each language, then language-specific intent prediction accuracy is improved, but operational cost increases
Solution Approach 1:
The universal machine learning model serves multiple languages with a single system, eliminating the need to operate and maintain multiple separate language-specific models. This reduces computational resource consumption, infrastructure costs, and operational overhead while maintaining the ability to accurately predict intents across different languages.
Solution Approach 2:
By merging multiple language processing functions into one model, the system reduces the total computational burden and operational expenses associated with running separate systems for each language, thereby lowering operational costs while preserving prediction accuracy.
3Measurement precision
If a chatbot is trained in a particular language, then intent prediction accuracy for that language is improved, but the chatbot becomes unable to service customers in other languages
Solution Approach 1:
The machine learning model is designed with multilingual capability from the outset, being trained on diverse training data that includes user inputs and intent labels in multiple languages. This enables the chatbot to maintain high intent prediction accuracy across different languages simultaneously, rather than being limited to a single language.
Solution Approach 2:
The model dynamically adapts to process multiple languages through its training on multilingual data, allowing it to flexibly handle various languages without requiring separate specialized models for each language, thereby improving both accuracy and language support capability.
Data Source
AI summary
A method includes: receiving a user query; generating first embedding data for the user query via a language agnostic machine learning embedding model; and predicting a first intent of the user query based on the first embedding data.