Cross-Lingual Neural Transfer via Layer Segmentation
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Solution Overview
Problem
Current natural language processing (NLP) systems are Anglo-centric, making it expensive and time-consuming to build models for non-English languages due to the reliance on vast amounts of high-quality annotated data and the difficulty in transferring models across languages effectively.
Innovation Solution
A cross-lingual neural model transfer framework that shares higher layers of neural networks between languages while maintaining separate language-specific embeddings, using parallel data and a loss function defined over task representations, eliminating the need for machine translation, bilingual dictionaries, and annotated data in the target language.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If model transfer is performed using traditional methods with machine translation and bilingual dictionaries, then language coverage can be expanded, but monetary and computational costs increase significantly
Solution Approach 1:
The patent copies the higher-level neural network layers from the source language model to the target language model, while keeping the embedding layers language-specific. This allows the model to leverage pre-trained representations from the source language without requiring expensive machine translation resources or bilingual dictionaries, thereby reducing computational costs while maintaining multilingual capability.
Solution Approach 2:
The patent segments the neural network into language-specific embedding layers and language-agnostic higher-level layers. This segmentation allows different parts of the model to be trained and transferred independently, enabling efficient cross-lingual transfer by only copying the higher-level layers while keeping embedding layers separate for each language.
2Measurement precision
If model transfer is performed using traditional methods with annotated data in target language, then model accuracy can be improved, but annotation costs and engineering time increase
Solution Approach 1:
The patent performs preliminary training of the neural network on source language annotated data before transferring to the target language. By pre-training the higher-level layers on source language data, the model acquires general linguistic representations that can be directly applied to the target language, eliminating the need for time-consuming annotation of target language data while maintaining model accuracy.
Solution Approach 2:
The higher-level neural network layers are designed to be universal and language-agnostic, capable of processing representations from any language. This universality allows the same higher-level layers to be transferred across multiple languages without requiring language-specific customization or additional annotation, reducing both time and cost.
3Reliability
If complete model retraining is performed for each language, then model performance can be optimized, but computational resources and training time increase
Solution Approach 1:
Instead of retraining the entire model for each target language, the patent copies only the higher-level layers from the source language model. This selective copying maintains model performance by preserving the learned representations while significantly reducing computational resources and training time compared to complete retraining.
Solution Approach 2:
The patent applies different training strategies to different parts of the model: the embedding layers are trained language-specifically to capture local linguistic characteristics, while the higher-level layers are copied and fine-tuned to maintain general performance. This local quality approach optimizes both performance and efficiency by applying the right training strategy to the right part of the model.
Data Source
AI summary
A task agnostic framework for neural model transfer from a first language to a second language, that can minimize computational and monetary costs by accurately forming predictions in a model of the second language by relying on only a labeled data set in the first language, a parallel data set between both languages, a labeled loss function, and an unlabeled loss function. The models may be trained jointly or in a two-stage process.