Neural Machine Translator With Shared Latent Space
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Solution Overview
Problem
Conventional machine translation systems face challenges in accurately and efficiently translating sentences between languages due to ambiguity in text and the need for separate neural networks for bidirectional translation, which can affect accuracy and consistency.
Innovation Solution
A computing system utilizing a neural machine translator with a shared latent space and a generative adversarial network (GAN) to concurrently generate parallel sentences in multiple languages, allowing for the learning of a manifold that enables simultaneous text generation in two or more languages by encoding and decoding coded representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate neural networks are used for bidirectional translation, then translation coverage is improved, but translation accuracy and consistency deteriorate
Solution Approach 1:
The patent merges multiple translation models into a single unified neural network that processes multiple languages simultaneously. This unified model shares latent spaces and parameters across language pairs, eliminating the need for separate bidirectional models while maintaining translation quality and consistency through shared representations.
Solution Approach 2:
The invention creates a universal translation system that can translate between any language pair using a single model. The system achieves multi-functionality by designing a language-agnostic architecture that adapts to different language pairs through shared latent spaces, eliminating the need for separate specialized models for each direction.
2Adaptability or versatility
If conventional machine translation systems are used, then translation capability is provided, but translation accuracy deteriorates due to text ambiguity
Solution Approach 1:
The patent introduces a shared latent space as an intermediary representation between source and target languages. This latent space captures semantic meanings in a language-agnostic form, allowing the system to resolve ambiguities by mapping to meaningful intermediate representations before generating target language translations.
Solution Approach 2:
The invention transitions from direct language-to-language translation to a higher-dimensional latent space representation. By embedding translations in a multi-dimensional latent space that captures semantic relationships, the system can disambiguate meanings and improve translation accuracy through this additional representational dimension.
3Adaptability or versatility
If separate models are used for each language pair, then language coverage is improved, but system complexity increases
Solution Approach 1:
The patent implements a universal translation model that handles multiple language pairs within a single system. This unified architecture provides multi-functionality by adapting to different language combinations through shared parameters and latent spaces, significantly reducing system complexity compared to maintaining separate models for each language pair.
Solution Approach 2:
The invention combines multiple translation models into one integrated system. By merging the processing of different language pairs into a single neural network with shared representations, the system achieves comprehensive language coverage while minimizing complexity through parameter sharing and unified architecture.
Data Source
AI summary
In at least one broad aspect, described herein are systems and methods in which a latent representation shared between two languages is built and/or accessed, and then leveraged for the purpose of text generation in both languages. Neural text generation techniques are applied to facilitate text generation, and in particular the generation of sentences (i.e., sequences of words or subwords) in both languages, in at least some embodiments.