Language Translation Using Image Embedding Space
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Solution Overview
Problem
Traditional language translation tools face challenges in accurately translating text inputs due to language idiosyncrasies, such as jargon and character complexities, leading to incorrect matchings and user frustration, especially when dealing with contextual content and multilingual queries.
Innovation Solution
A computer-implemented method that associates a source language text string with a text string vector in an embedded set, identifying a closer vector in the set associated with a target language, using image vectors to maintain contextual content and semantic meaning, and returning the translated text string, thereby overcoming traditional sequence-to-sequence translation limitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sequence-to-sequence translation is used, then the translation process is simple, but translation accuracy deteriorates due to language idiosyncrasies and contextual nuances
Solution Approach 1:
The patent introduces an image embedding space as an intermediary between source and target languages. Instead of direct text-to-text translation, the system translates text to image embeddings and then to target language text, using images as a universal mediator that captures semantic meaning beyond linguistic limitations
Solution Approach 2:
The patent transforms the translation problem from a 1D text sequence mapping to a multi-dimensional image embedding space. By projecting text into visual feature spaces and utilizing spatial relationships in the embedding, the system captures contextual nuances that linear text-based approaches miss
2Adaptability or versatility
If textual dictionary translation is used, then the system is easy to implement, but it cannot overcome language idiosyncrasies such as jargon and popular expressions
Solution Approach 1:
Images serve as a universal intermediary that transcends language-specific expressions. The system converts text with idioms and jargon into image embeddings that capture the visual essence of the meaning, then retrieves target language text that conveys the same visual concept, effectively bridging cultural and linguistic gaps
Solution Approach 2:
The system changes the representation parameters from text-based lexical matching to visual feature-based matching. By transforming text into image embedding vectors and utilizing spatial distance metrics in the embedding space, the system adapts to handle diverse linguistic expressions through a unified visual parameter space
3Ease of operation
If language detection routines are used, then automatic language selection is achieved, but incorrect detection leads to incorrect matchings
Solution Approach 1:
The image embedding space acts as a language-agnostic intermediary that bypasses the need for accurate language detection. Since images represent visual concepts universally, the system can translate between languages without needing to correctly identify the source or target language, using the visual embedding as a common reference frame
Data Source
AI summary
A method for receiving a first text in a source language is provided. The method includes associating the first text in the source language with a first vector, selected from a plurality of vectors associated with the source language in an embedded set, and identifying a second vector that is closer to the first vector than a pre-selected threshold. The second vector is associated with a second text in a target language. The method includes associating the first text in the source language with the second text in the target language, wherein the embedded set includes a first image vector for a first image and a second image vector for a second image, and returning the second text in the target language, the second text being a translation of the first text from the source language to the target language. A system configured to perform the above method is also provided.