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 by determining monolingual and language weights, then calculates multilingual probabilities to provide word predictions across multiple languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single multilingual language model is trained to handle multiple languages, then language identification and word prediction accuracy 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. This segmentation reduces the computational burden on each individual model while collectively covering multiple languages, resolving the contradiction between accuracy and computational power.
Solution Approach 2:
The patent introduces a language identification module as an intermediary that determines which monolingual language model to use based on the input language. This mediator enables the system to achieve multilingual capability without requiring a single computationally expensive multilingual model.
2Power
If separate monolingual language models are used for each language, then computational requirements are reduced to fit contemporary systems, but language identification and word prediction effectiveness deteriorates in multilingual contexts
Solution Approach 1:
The language identification module serves as an intermediary that selects the appropriate monolingual language model based on the input language, ensuring that each query is processed by the most suitable model and maintaining prediction effectiveness.
Solution Approach 2:
The system achieves universal multilingual capability by combining multiple specialized monolingual models with a language identification layer, allowing the system to handle multiple languages effectively without requiring each model to be universally trained on all languages.
3Ease of manufacture
If existing monolingual language models are used without modification, then implementation simplicity is maintained, but effectiveness in multilingual communication scenarios is insufficient
Solution Approach 1:
The language identification module is introduced as an intermediary layer that sits between the user input and the monolingual language models. This addition enables multilingual handling while keeping the underlying monolingual models unchanged and simple to implement.
Solution Approach 2:
The system segments the language processing task into two distinct parts: language identification (handled by the intermediary module) and language-specific processing (handled by the simple monolingual models). This segmentation maintains implementation simplicity while improving multilingual effectiveness.
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, receiving context information associated with a current word; determining, for each of a plurality of languages, a set of monolingual probabilities based on the context information; determining a set of language weights based on the context information; determining a set of multilingual probabilities based on the respective sets of monolingual probabilities and the set of language weights; and providing a plurality of candidate words based on the set of multilingual probabilities.