Machine Translation via Interlingua and Dependency Trees
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Solution Overview
Problem
Current machine translation systems are overly complex and inefficient, with each new level of complexity providing minimal gains in quality while increasing complexity, and they struggle to accurately translate between languages due to excessive expressive capacity over semantically unimportant features and reliance on human languages as intermediaries, leading to information loss.
Innovation Solution
A machine translation system that uses marked-lemma dependency trees (MDTs) associated with an interlingua, which is a non-human language, to convert input representations between languages, allowing for more accurate and simpler translation by leveraging a semantic space configuration that includes singular, nested, or parallel semantic spaces, and employing a topic stack for context awareness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current machine translation systems use human languages as intermediaries and employ complex models with excessive expressive capacity, then they can handle diverse linguistic features, but they suffer from information loss and increased complexity
Solution Approach 1:
The patent introduces an interlingua as an intermediary representation that is language-agnostic and captures semantic meaning without being tied to any specific human language. This interlingua serves as a mediator between source and target languages, eliminating the need for complex human language-based intermediate representations and reducing information loss while maintaining adaptability to linguistic diversity
Solution Approach 2:
The patent extracts and separates the semantic content from the linguistic form by using an interlingua that represents meaning independently of any human language. This extraction allows the system to handle diverse linguistic features without being constrained by the limitations or complexities of specific human languages, thereby reducing overall system complexity
2Measurement precision
If machine translation systems add more complexity with techniques like Good-Turing smoothing, syntactic-tree reordering, and weighted finite state machines, then translation quality improves slightly, but the model becomes overly complicated
Solution Approach 1:
The patent changes the fundamental parameter of representation from human language-specific structures to a language-agnostic interlingua. This parameter change allows the system to achieve high translation quality through simpler, more direct semantic mappings without requiring multiple layers of complex processing techniques
Solution Approach 2:
Instead of starting with human languages and trying to map between them directly, the patent inverts the approach by first translating to an interlingua representation and then from there to the target language. This inversion simplifies the translation process by removing the complexity of direct human language-to-human language mapping
3Ease of manufacture
If the system uses human languages as interlingua, then it leverages existing linguistic structures, but it introduces information loss and limitations from the interlingua language itself
Solution Approach 1:
The patent uses an interlingua as a mediator that is specifically designed to avoid the information loss inherent in using human languages as intermediaries. This interlingua captures semantic meaning without being subject to the grammatical, syntactic, and lexical limitations of any specific human language, thereby preserving more information throughout the translation process
Data Source
AI summary
Systems and methods for machine translation use a novel interlingua comprising a topic stack and a weighted set of marked lemma dependency trees.