Multilingual Command Recommendation Using Shared Query Embeddings
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Solution Overview
Problem
Content creation applications face challenges in providing accurate and efficient in-application command recommendations across multiple languages due to the use of different machine-learning models for each language, leading to unbalanced user experiences and significant processing and maintenance costs.
Innovation Solution
A unified multilingual command recommendation model using a multilingual encoder and transformer-based deep learning model maps different languages into a shared embedding space, enabling a single model to provide command recommendations across languages, with a query processing architecture that collects data for ongoing training and uses a confidence score to categorize recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate machine-learning models are used for each language to provide command recommendations, then language-specific accuracy can be maintained, but the number of models increases significantly leading to higher processing and maintenance costs
Solution Approach 1:
The patent applies a single multilingual machine-learning model that can handle multiple languages (English, Spanish, French, German, Chinese, Japanese, Korean, Portuguese) instead of separate models for each language. This universal model uses a multilingual encoder that maps different languages into a shared embedding space, enabling one model to perform the function previously requiring many language-specific models, thus reducing complexity while maintaining multilingual capability
Solution Approach 2:
The patent merges multiple language-specific models into a single unified multilingual model. The multilingual encoder combines embeddings from different languages into a shared representation space, allowing the system to process queries in any supported language using the same model infrastructure, thereby consolidating what were previously separate model systems into one integrated solution
2Reliability
If separate machine-learning models are used for each language, then language-specific optimization is possible, but maintenance costs and processing overhead increase significantly
Solution Approach 1:
The multilingual model provides universal reliability across languages by using a shared encoder architecture that has been trained to handle multiple languages simultaneously. The model maintains reliable performance in languages other than English through the multilingual encoder's ability to map different languages into a shared embedding space, eliminating the need for separate optimized models for each language while reducing processing and maintenance overhead
3Device complexity
If a single multilingual model is used for all languages, then model complexity and maintenance costs are reduced, but accuracy for non-primary languages may be compromised
Solution Approach 1:
The patent applies parameter changes by using a multilingual encoder that transforms input text from different languages into a shared embedding space with adjusted dimensional parameters. The encoder maps languages such as English, Spanish, French, German, Chinese, Japanese, Korean, and Portuguese into a unified representation space, allowing the single model to accurately process and recommend commands for all languages through parameter transformation rather than requiring separate models for each language
Data Source
AI summary
A method and system for one or more application command recommendations may include receiving a search query, the search query being an in-application assistance query, accessing contextual data associated with the search query, providing at least one of the search query and the contextual data as input to a multilingual machine-learning (ML) model to identify one or more application command recommendations, obtaining the one or more application command recommendations as an output from the multilingual ML model, and providing data about the output to the application for display. The multilingual ML model uses a multilingual encoder to provide command recommendations for a plurality of languages.