Multi-media Context Language Processing for Translation Accuracy
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Solution Overview
Problem
Existing language processing engines struggle to accurately process natural language due to the lack of consideration for contextual information, leading to inaccuracies in translation and classification tasks, especially in cases where context-dependent terms like 'lift' differ between languages or dialects.
Innovation Solution
Incorporating multi-media context data into machine learning models for language processing, which includes object, location, or author-provided labels, to enhance the accuracy of translation, correction, and tagging engines by training models with data that accounts for the contextual nuances of content items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional language processing engines use dictionary translations and rule-based approaches, then processing speed is maintained, but translation accuracy deteriorates due to inability to account for contextual information
Solution Approach 1:
The patent transitions from traditional 1D text-based processing to multi-dimensional processing by incorporating images, audio recordings, and video clips associated with content items. This additional dimensional context enables the system to disambiguate words like 'lift' by analyzing visual content, thereby improving translation accuracy without relying solely on complex linguistic rules
Solution Approach 2:
The system performs preliminary analysis of multi-media context (images, audio, video) before executing the translation or language processing task. By pre-processing and understanding the contextual elements in advance, the system can make more accurate processing decisions, reducing the need for complex post-processing corrections
2Measurement precision
If language processing considers contextual information from multi-media, then processing accuracy improves, but computational resources and processing time increase
Solution Approach 1:
The system segments the language processing task into distinct phases: extracting multi-media context from associated images/audio/video, analyzing the contextual information, and applying it to the language processing task. This segmentation allows for optimized processing at each stage, preventing bottlenecks and reducing overall processing time while maintaining accuracy
3Reliability
If machine learning models are trained with multi-media context data, then language processing reliability improves, but model training complexity and data requirements increase
Solution Approach 1:
The system employs a universal machine learning model architecture that can process multiple types of input data (text, images, audio, video) through a unified framework. This multi-functional approach allows the same model structure to handle diverse multi-media contexts, reducing the need for separate specialized models and simplifying the overall training complexity while improving reliability across different content types
Data Source
AI summary
Technology is disclosed that improves language processing engines by using multi-media (image, video, etc.) context data when training and applying language models. Multi-media context data can be obtained from one or more sources such as object/location/person identification in the multi-media, multi-media characteristics, labels or characteristics provided by an author of the multi-media, or information about the author of the multi-media. This context data can be used as additional input for a machine learning process that creates a model used in language processing. The resulting model can be used as part of various language processing engines such as a translation engine, correction engine, tagging engine, etc., by taking multi-media context/labeling for a content item as part of the input for computing results of the model.