Multilingual Embedding Alignment for Cross-Domain NLP
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing natural language understanding systems face challenges in scaling to multiple languages and domains due to language-dependent preprocessing and feature engineering techniques, requiring significant resources and domain expertise, and are unable to generalize effectively across different linguistic and semantic contexts.
Innovation Solution
A multi-lingual/domain embedding system aligns embeddings from various languages and domains using parallel vocabularies to generate a transformation matrix, creating cross-domain, multilingual embeddings that can be used to build language and domain-independent artificial intelligence models, enabling applications in new languages and domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional language-dependent preprocessing and feature engineering techniques are used, then models can be trained effectively in specific languages and domains, but the models cannot be scaled to multiple languages and domains without significant resources and domain expertise
Solution Approach 1:
The patent creates a universal embedding space that can represent multiple languages and domains simultaneously. By aligning embeddings from different languages and domains into a shared vector space, the system enables a single model to process and understand diverse linguistic and domain-specific data without requiring separate models for each language or domain, thus achieving multi-functionality.
Solution Approach 2:
The patent transforms the parameter representation by converting language-specific and domain-specific embeddings into a unified embedding space through alignment techniques. This involves changing the parameter space from multiple separate embedding spaces to a single shared embedding space, allowing the model to generalize across languages and domains by modifying how data is represented rather than changing the model architecture.
2Productivity
If translation techniques are applied to translate data from existing language to another language, then language coverage can be expanded, but human translation is labor-intensive and time-consuming while machine translation can be costly and unreliable
Solution Approach 1:
The patent replaces traditional translation mechanisms (both human and machine translation systems) with an embedding alignment approach. Instead of translating text from one language to another, the system aligns embeddings from different languages into a shared space, allowing the model to understand multiple languages through their vector representations rather than through translation, thereby eliminating the need for translation while maintaining accuracy.
3Measurement precision
If pre-trained models are customized for specific tasks, then task performance can be improved, but this requires domain expertise and extensive resources
Solution Approach 1:
The patent creates a universal embedding space that can represent multiple languages and domains simultaneously. By aligning embeddings from different languages and domains into a shared vector space, the system enables a single model to process and understand diverse linguistic and domain-specific data without requiring separate models for each language or domain, thus achieving multi-functionality.
Solution Approach 2:
The patent performs preliminary alignment of embeddings from different languages and domains into a shared embedding space before task-specific modeling. This pre-alignment creates a unified representation that can be directly used for various tasks without requiring extensive customization, thereby reducing the resources and expertise needed for task adaptation while maintaining high performance.
Data Source
AI summary
A method, system and a computer program product are provided for aligning embeddings of multiple languages and domains into a shared embedding space by transforming monolingual embeddings into a multilingual embeddings in a first shared embedding space using a cross-lingual learning process, and then transforming the multilingual embeddings into cross-domain, multilingual embeddings in a second shared embedding space using a cross-domain learning process.