Cross-Lingual Semantic Label Space Transformation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing techniques for multilingual semantic role labeling struggle to accurately determine semantic relationships between argument labels across different languages, often relying on English language projections which can lose language-specific details and fail to account for varying label annotations and omissions across languages.
Innovation Solution
A system and method that uses machine learning to generate a defined label space, enabling the detection of semantic similarity and difference relationships between labels in multiple languages, allowing for bidirectional projection and better understanding of text comparisons across languages without relying solely on English language annotations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If English language projection is used to determine semantic relationships across languages, then implementation simplicity is improved, but measurement precision of semantic relationships deteriorates
Solution Approach 1:
The patent introduces an intermediary computation component that learns language-specific label spaces and transforms them into a shared semantic space. This mediator enables accurate cross-lingual semantic relationship determination without direct English projection, resolving the contradiction by maintaining both implementation feasibility and measurement precision through a structured transformation approach.
Solution Approach 2:
The system changes the parameter representation by learning language-specific label spaces (e.g., Spanish label space, English label space) and their transformations to a shared space. This parameter transformation approach allows accurate semantic relationship determination across languages while maintaining implementation simplicity through standardized computational processes.
2Measurement precision
If language-specific label variations are accounted for, then semantic relationship accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the multilingual semantic relationship determination into distinct components: language-specific label space learning for each language, transformation component for mapping between spaces, and computation component for determining relationships. This segmentation manages complexity by breaking down the overall system into modular, language-independent components that can be independently trained and combined.
Solution Approach 2:
The transformation component serves multiple functions by handling label space transformations across different language pairs. This universal component can transform from any language-specific label space to the shared semantic space and vice versa, reducing overall system complexity compared to having separate transformation mechanisms for each language pair.
3Adaptability or versatility
If bidirectional projection between languages is implemented, then adaptability across languages is improved, but computation time increases
Solution Approach 1:
The system performs preliminary action by pre-learning language-specific label spaces and their transformations to the shared semantic space during a training phase. Once these transformations are established, the actual semantic relationship determination can proceed efficiently without real-time computation of language-specific mappings, reducing computation time while maintaining bidirectional adaptability.
Data Source
AI summary
One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to regularizing semantic similarity relationships relative to a pair of languages. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise a computation component that generates a transformation comprising a semantic similarity relationship between detected semantic labels of a pair of languages.