Multilingual Word Prediction via Segmented Monolingual Models
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Solution Overview
Problem
Existing monolingual language models are inadequate for effective language identification and word prediction in multilingual communication, and training a single multilingual language model exceeds the computational capabilities of contemporary systems.
Innovation Solution
An electronic device identifies context information and generates candidate words in multiple languages, allowing for the prediction of words in one language while considering the presence of words from another language, using a digital assistant system that includes client-side and server-side components for processing and generating responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single multilingual language model is trained to handle multiple languages, then language identification and word prediction capability is improved, but computational requirements exceed the capabilities of contemporary systems
Solution Approach 1:
The patent divides the multilingual language model into multiple separate monolingual language models, each trained on a single language. The system segments the multilingual processing task into multiple monolingual processing streams, allowing each model to be smaller and more computationally efficient while collectively handling multiple languages through language identification routing.
2Measurement precision
If a single multilingual language model is trained to handle multiple languages, then word prediction accuracy in multilingual contexts is improved, but the scale and complexity of training exceeds computational capabilities
Solution Approach 1:
The patent segments the large-scale multilingual model training into multiple smaller monolingual model training tasks. Each monolingual model is trained independently on its respective language corpus, reducing the complexity and computational burden of each training task while maintaining overall word prediction accuracy through the collective capability of multiple specialized models.
Solution Approach 2:
The patent introduces language identification as an intermediary component that determines which monolingual model to use for a given input. This intermediary layer enables the system to achieve multilingual word prediction capability without requiring a single large multilingual model, thereby reducing training complexity while maintaining prediction accuracy.
3Power
If monolingual language models are used for multilingual communication, then computational burden is reduced, but language identification and word prediction effectiveness deteriorates
Solution Approach 1:
The patent creates a universal language model system where multiple monolingual models work together to handle multilingual communication. The language identification component provides multi-functionality by routing inputs to the appropriate monolingual model, enabling the system to maintain computational efficiency of monolingual models while achieving the reliability of multilingual language identification and word prediction.
Data Source
AI summary
Systems and processes for multilingual word prediction are provided. In accordance with one example, a method includes, at an electronic device having one or more processors and memory, identifying context information of the electronic device and generating, with the one or more processors, a plurality of candidate words based on the context information, wherein a first candidate word of the plurality of candidate words corresponds to a first language of a plurality of languages and a second candidate word of the plurality of candidate words corresponds to a second language of the plurality of languages different than the first language.