Machine Translation via Formal Source Language Intermediary

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Solution Overview

Problem

Current machine translation methods fail to achieve substantial breakthroughs in translation quality due to the lack of formalization of non-formal source languages, leading to incomplete and ambiguous translations, especially across different languages.

Innovation Solution

A novel machine translation method that formalizes non-formal source languages through interactive or automatic processes, identifying and tagging fixed language segments with meaning marks, and transforming them into formal or non-formal target languages using predefined rules, ensuring accurate and unambiguous translations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Manufacturing precision

If direct transformation or intermediate language methods are used for machine translation, then translation speed is maintained, but translation quality remains incomplete and ambiguous

Engineering Contradiction:

Improvetranslation qualityVSAvoidinformation completeness

Core Design Contradiction:

Manufacturing precisionVSLoss of information

Solution Approach 1:

The patent introduces a formal language as an intermediary representation between the source natural language and the target natural language. The formal language includes structured components such as formal words, formal phrases, and formal sentences with explicit semantic relationships, serving as a mediator that preserves complete information during translation while enabling high-quality output.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent segments the translation process into distinct stages: natural language analysis, formal language conversion, and target language generation. Each stage processes specific linguistic components separately, allowing for precise control over information preservation and translation quality at each step of the transformation chain.

Inventive Principle:

Principle #1Segmentation

2Manufacturing precision

If formalization processing is added to the machine translation process, then translation accuracy improves, but system complexity increases

Engineering Contradiction:

Improvetranslation accuracyVSAvoidsystem complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The formal language system is designed to be self-descriptive and self-organizing, where the structured formal representations automatically encode semantic relationships and linguistic rules. This self-service property reduces the need for external complex processing mechanisms, as the formal language structure itself carries the necessary information for accurate translation.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent changes the parameter of language representation from informal natural language to formal structured language with defined grammatical and semantic parameters. This parameter change enables precise control over translation accuracy while the formal structure inherently manages the complexity through its organized representation system.

Inventive Principle:

Principle #35Parameter changes

3Manufacturing precision

If existing machine translation methods are used, then implementation is straightforward, but translation quality achieves no substantial breakthrough

Engineering Contradiction:

Improvetranslation qualityVSAvoidimplementation simplicity

Core Design Contradiction:

Manufacturing precisionVSEase of manufacture

Solution Approach 1:

The patent performs preliminary action by converting the source natural language into a formal language representation before generating the target language. This preliminary formalization step establishes a structured intermediate form that captures complete semantic information, enabling subsequent translation to achieve high quality while the pre-established formal structure simplifies the overall implementation process.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS8990067B2Machine translation into a target language by interactively and automatically formalizing non-formal source language into formal source language

Publication Date: 2015.03.24 CHENG GUANGYUAN

AI summary

A machine translation method and system comprises the steps of (a) formalizing a non-formal source language in an interactive or automatic way and (b) transforming the formal source language into a formal or non-formal target language in an automatic way. It eliminates the language barrier between person and person and the language barrier between person and computer: A user translates his/her non-formal native language correctly and without lexical ambiguity into any non-formal foreign language which he/she knows nothing about; a user and a computer exchange information in his/her non-formal native language correctly and without lexical ambiguity. It can be used in network terminal equipment, Internet knowledge bases, knowledge reasoning search engines, expert systems and automatic programming. That formalization of a source language is the common foundation for transformation into various target languages makes it especially suitable for multilingual machine translation.