Implicit Bridging for Zero-Shot Machine Translation
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Solution Overview
Problem
Traditional machine translation systems require large amounts of parallel training data, which is challenging to obtain for languages with limited resources, leading to issues like error propagation, increased latency, and system complexity, especially when translating between languages with less internet presence like Japanese and Korean.
Innovation Solution
The implementation of a zero-shot translation system that trains a machine learning model to perform machine translation tasks without explicit training data for specific tasks, using implicit bridging to translate text segments from one language to another without requiring explicit bridging through a third language, thereby reducing errors, latency, and system complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional machine translation systems use explicit bridging through a third language, then translation can be performed between languages with limited resources, but error propagation, increased latency, and system complexity occur
Solution Approach 1:
The patent introduces a language identifier token as an intermediary that guides the translation model to implicitly bridge between source and target languages without requiring explicit intermediate language steps. The model learns to map source language directly to target language through the identifier token, avoiding error propagation while maintaining the ability to translate between languages with limited resources.
Solution Approach 2:
The patent segments the translation task into two independent components: language identification (via the identifier token) and translation execution. This segmentation allows the model to handle language pairing separately from the translation process, reducing system complexity while maintaining versatility across multiple language pairs.
2Adaptability or versatility
If traditional machine translation systems use explicit bridging through a third language, then translation can be performed between languages with limited resources, but translation accuracy decreases due to error propagation
Solution Approach 1:
The language identifier token serves as a mediator that enables direct mapping between source and target languages without requiring intermediate translation steps. This eliminates error propagation while maintaining the ability to translate between languages with limited resources, as the model learns direct language pairs through the identifier token guidance.
3Adaptability or versatility
If traditional machine translation systems use explicit bridging through a third language, then translation can be performed between languages with limited resources, but processing time increases due to increased latency
Solution Approach 1:
The language identifier token enables direct translation paths by guiding the model to map source language directly to target language, eliminating the need for intermediate translation steps. This reduces processing time and latency while maintaining the ability to translate between languages with limited resources.
4Adaptability or versatility
If a machine learning model is trained on multiple machine learning tasks with augmented model inputs, then the model can perform various tasks including zero-shot translation, but training data requirements increase
Solution Approach 1:
The patent implements a universal translation model that handles multiple language pairs and tasks through a single architecture. By using language identifier tokens to specify target languages, the model achieves multi-functionality without requiring separate models for each language pair, reducing overall training data requirements while maintaining versatility.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media for performing machine learning tasks. One method includes receiving (i) a model input, and (ii) data identifying a first machine learning task to be performed on the model input to generate a first type of model output for the model input; augmenting the model input with an identifier for the first machine learning task to generate an augmented model input; and processing the augmented model input using a machine learning model, wherein the machine learning model has been trained on training data to perform a plurality of machine learning tasks including the first machine learning task, and wherein the machine learning model has been configured through training to process the augmented model input to generate a machine learning model output of the first type for the model input.