Language Model Construction via Multi-Application Input Aggregation
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Solution Overview
Problem
Current language processing applications face challenges in constructing effective language models that can reliably process natural language inputs, leading to inconsistencies and inaccuracies in applications such as speech recognition and translation due to variations in linguistic characteristics and contexts.
Innovation Solution
A method and system for constructing a language model by processing language-based inputs through multiple Language Processing (LP) applications, where inputs are transformed and outputs are used to generate a model encapsulating linguistic characteristics, allowing for improved processing and model refinement based on corpus data and user contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a language model is constructed using traditional methods with limited data sources, then the model construction process is simple, but the accuracy and reliability of language processing is insufficient
Solution Approach 1:
The patent combines multiple language processing applications (speech recognition, handwriting recognition, optical character recognition, language translation) into a unified system that processes inputs through multiple applications and aggregates their outputs to construct a comprehensive language model, thereby improving accuracy while managing complexity through integration
Solution Approach 2:
The language model construction system is designed to handle multiple types of language-based inputs (speech, handwriting, text) and generate outputs that can be used across different language processing applications, making the system universally applicable and improving overall accuracy through multi-functional data aggregation
2Adaptability or versatility
If language processing applications use a single language model, then the system complexity is low, but the ability to handle variations in linguistic characteristics and contexts is limited
Solution Approach 1:
The system dynamically selects and aggregates outputs from different language processing applications based on the type of input received, allowing the language model to adapt to varying linguistic characteristics and contexts while maintaining a manageable system structure through conditional processing
3Measurement precision
If multiple language processing applications process the same input, then the language model accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The system continuously processes inputs through multiple language processing applications in parallel and aggregates their outputs to construct and refine the language model, maintaining continuous improvement of accuracy while optimizing processing time through efficient parallel execution and data aggregation
Data Source
AI summary
Disclosed herein are various embodiments of methods and systems for constructing a first language model for use by a first Language Processing (LP) application of a plurality of LP applications. Each LP application of the plurality of LP applications receives one or more of a language based input, a derivative of the language based input, a response to the language based input and a derivative of the response. The method includes processing at least one input by a second LP application of the plurality of LP applications. Based on the processing of the second LP application, at least one output is generated. Subsequently, at least a portion of the first language model is constructed based on the at least one output.