AI Text Translation Using Generated Visual Representations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional translation systems require paired text and image inputs during inference, limiting their applicability to real-world scenarios.
Innovation Solution
A multimodal machine translation framework that incorporates images at training time to enhance text-only models using AI techniques like LSTM and transformers, leveraging autoregressive visual representation transformers and discrete visual encodings to generate visual tokens for translation, and employing Gumbel-Softmax relaxation for optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If paired text and image inputs are required during inference, then translation accuracy can be improved, but system complexity and input requirements increase
Solution Approach 1:
The system performs visual representation generation during the training phase rather than requiring real images during inference. The visual encoder and autoregressive transformer are pre-trained to generate visual representations from text alone, so the translation model learns to translate using generated visual tokens without needing actual images at test time. This preliminary action resolves the contradiction by preparing the necessary visual representations in advance during training.
Solution Approach 2:
The system creates synthetic visual representations as copies of what would be extracted from real images, but generates them from text using the autoregressive transformer. These generated visual tokens serve as substitutes for actual image inputs during inference, allowing the translation model to operate without requiring real images while maintaining the benefits of visual information.
2Measurement precision
If real-time image input is required, then visual context accuracy is improved, but applicability to real-world scenarios deteriorates
Solution Approach 1:
The system enables text-only input to serve itself by generating the necessary visual representations through the autoregressive transformer. The translation model receives both text input and generated visual tokens, allowing it to function autonomously without external image inputs. This self-service mechanism makes the system adaptable to real-world scenarios where paired text-image inputs are not always available.
3Measurement precision
If visual representations are generated during inference, then translation quality improves, but processing time increases
Solution Approach 1:
The visual representation generation capability is pre-trained during the training phase using the autoregressive transformer and visual encoder. This preliminary training allows the system to generate visual representations efficiently during inference without adding significant processing time, as the generation process has already been optimized through training. The model learns to generate visual tokens rapidly as part of the translation pipeline.
Data Source
AI summary
Methods, systems, and computer program products for translating text using generated visual representations and artificial intelligence are provided herein. A computer-implemented method includes generating a tokenized form of at least a portion of input text in a first language; generating at least one visual representation of at least a portion of the input text using a first set of artificial intelligence techniques; generating a tokenized form of at least a portion of the at least one visual representation; and generating an output including a translated version of the input text into at least a second language by processing, using a second set of artificial intelligence techniques, at least a portion of the tokenized form of the at least a portion of the input text and at least a portion of the tokenized form of the at least a portion of the at least one visual representation.