Cross-lingual Neural Network Training via Contrastive Embedding Alignment
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Solution Overview
Problem
Current cross-lingual pretrained language models face challenges in effectively transferring task knowledge to unresourced languages due to the lack of labeled data, relying on translation methods that are inefficient and require significant computational resources, and often result in limited zero-shot performance.
Innovation Solution
A method for cross-lingual training that involves receiving input data elements in multiple languages, forming representations, and adapting a neural network model using similarity-based losses to align embeddings across languages, enabling improved zero-shot performance without the need for labeled data in target languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If translation methods are used to train cross-lingual models, then multilingual capability is achieved, but computational resources and time are significantly consumed
Solution Approach 1:
The model performs preliminary contrastive learning on parallel sentence pairs to establish cross-lingual alignment before actual task training. This preliminary alignment of sentence embeddings across languages enables faster convergence and reduces the computational burden during subsequent task-specific training phases.
2Adaptability or versatility
If translation methods are used to train cross-lingual models, then multilingual capability is achieved, but computational resources are significantly consumed
Solution Approach 1:
The method extracts and utilizes existing parallel sentence pairs from available training data to perform contrastive learning, rather than requiring full translation of entire datasets. This extraction of essential alignment information reduces computational resource requirements while maintaining multilingual capability.
3Measurement precision
If labeled data is required for model training in each language, then training precision is improved, but data availability decreases for under-resourced languages
Solution Approach 1:
The model uses an intermediary contrastive learning mechanism that operates on parallel sentence pairs to bridge the gap between languages with labeled data and languages without labeled data. This intermediary alignment process enables knowledge transfer from resource-rich languages to under-resourced languages without requiring labeled training data in the target languages.
4Manufacturing precision
If Fastalign is used to match words in translated sentences, then word alignment is achieved, but zero-shot performance improvement is limited
Solution Approach 1:
Instead of relying solely on word-level alignment in the traditional dimension, the method introduces a sentence embedding dimension through contrastive learning. This additional dimensional approach captures semantic relationships at the sentence level, providing more robust cross-lingual transfer that improves zero-shot performance beyond what word-level alignment alone can achieve.
Data Source
AI summary
Described is an apparatus and method for cross-lingual training between a source language and at least one target language. The method comprises receiving a plurality of input data elements, training a neural network model by repeatedly: i. selecting one of the plurality of input data elements; ii. obtaining a first representation of the first linguistic expression of the selected input data element by means of the neural network model; iii. obtaining a second representation of the second linguistic expression of the selected input data element by means of the neural network model; iv. forming a first loss in dependence on the performance of the neural network model on the first linguistic expression; v. forming a second loss indicative of a similarity between the first representation and the second representation; and vi. adapting the neural network model in dependence on the first and second losses.