Unified Text Conversion and Prediction Framework
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Solution Overview
Problem
Inputting text that requires conversion from one writing system to another, such as Pinyin to Chinese Hanzi, is inefficient due to the lack of integration between text conversion and prediction stages, leading to suboptimal ranking schemes and limited accuracy.
Innovation Solution
A unified text conversion and prediction framework using a single end-to-end neural network with stacked sub-networks for character and word-level processing, allowing joint optimization and seamless integration of character conversion with word prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If text conversion and prediction are performed as separate operations, then the system structure is simpler, but the accuracy and robustness of text conversion and prediction deteriorate
Solution Approach 1:
The patent merges text conversion and text prediction into a unified end-to-end neural network framework. The character-level sub-network handles text conversion from phonetic to target writing system, while the word-level sub-network performs text prediction, with both sub-networks jointly optimized through a single loss function. This integration allows information to flow seamlessly between conversion and prediction stages, improving accuracy and robustness while maintaining manageable system complexity through modular sub-network architecture.
2Reliability
If text conversion and prediction are tightly coupled in a unified framework, then accuracy and robustness improve, but the device complexity increases
Solution Approach 1:
The unified framework is segmented into distinct character-level and word-level sub-networks, each with specialized functions. The character-level sub-network focuses on conversion accuracy by modeling character sequences, while the word-level sub-network focuses on prediction accuracy by modeling word sequences. This segmentation allows each component to be optimized independently while benefiting from joint training, managing complexity through functional decomposition.
Solution Approach 2:
The patent implements a nested architecture where the character-level sub-network and word-level sub-network are integrated within a single end-to-end framework. The character-level processing outputs feed into the word-level processing, creating a hierarchical structure where smaller units (characters) are nested within larger units (words). This nesting allows information to flow from character to word level while maintaining a unified optimization objective.
3Reliability
If a single end-to-end neural network is used for unified processing, then joint optimization is achieved, but the training complexity increases
Solution Approach 1:
The patent employs dynamic training strategies for the end-to-end neural network, including adaptive learning rate scheduling and gradient clipping to handle the complexity of joint optimization. The unified loss function dynamically balances contributions from character-level conversion loss and word-level prediction loss during training, allowing the system to adapt to different stages of learning and maintain stable convergence despite the increased training complexity.
Data Source
AI summary
The present disclosure generally relates to integrated text conversion and prediction. In an example process, a current character input of a first writing system is received. A first current character context in the first writing system is determined based on the current character input and a first previous character context in the first writing system. A second current character context in a second writing system is determined based on the first current character context, a second previous character context in the second writing system, and a character representation in the second writing system. A current word context in the second writing system is determined based on the second current character context, a previous word context in the second writing system, and a word representation in the second writing system. Based on the current word context, a probability distribution over a word inventory in the second writing system is determined.