Cross-lingual Sentence Alignment via Pretrained Multilingual Models
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Solution Overview
Problem
Existing cross-lingual sentence alignment models face challenges in achieving accurate zero-shot transfer for low-resource languages due to the disparity in training data availability, with unsupervised approaches offering low accuracy and supervised methods requiring difficult-to-acquire parallel data.
Innovation Solution
A cross-lingual sentence alignment framework is developed, utilizing a pretrained multi-lingual language model like XLM-RoBERTa, trained only on rich-resource language pairs, which includes a BERT score computation module and normalization layer to enhance accuracy and efficiency, allowing for zero-shot cross-lingual transfer with low-resource languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised training methods are used for cross-lingual sentence alignment, then alignment accuracy is improved, but the requirement for parallel training data becomes difficult to satisfy for low-resource languages
Solution Approach 1:
The patent introduces an intermediary approach by using a pretrained multilingual language model as a bridge between source and target languages. Instead of directly training on parallel data between two low-resource languages, the system uses a multilingual model that has been pretrained on multiple languages to mediate the alignment process, enabling zero-shot cross-lingual transfer without requiring parallel training data for the specific language pair
Solution Approach 2:
The system performs preliminary action by pretraining a multilingual language model on multiple languages before applying it to cross-lingual sentence alignment. This pretraining phase prepares the model to handle various languages and their linguistic patterns, which then enables accurate alignment for low-resource language pairs without requiring specific parallel training data for those pairs
2Quantity of substance
If unsupervised approaches are used for cross-lingual alignment, then data requirements are reduced, but alignment accuracy deteriorates
Solution Approach 1:
The system performs preliminary supervised pretraining on available parallel data for rich-resource language pairs, which prepares the model to achieve high accuracy. This preliminary action with quality data enables the model to subsequently perform zero-shot transfer to low-resource languages without requiring unsupervised approaches, thus maintaining high alignment accuracy while reducing data requirements for the target language pairs
3Adaptability or versatility
If models are trained on all language pairs, then comprehensive language coverage is achieved, but data efficiency decreases and training complexity increases
Solution Approach 1:
The patent applies universality by training a single multilingual language model that can handle multiple languages and language pairs simultaneously. Instead of training separate models for each language pair, the universal multilingual model achieves comprehensive language coverage while improving data efficiency by learning from the diversity of multiple languages in a unified framework
Solution Approach 2:
The system extracts and leverages the multilingual capabilities from a pretrained model, separating the language understanding skills from specific language pair training. This extraction allows the model to apply learned linguistic patterns across different language pairs without requiring extensive parallel data for each pair, thus improving data efficiency while maintaining versatility
Data Source
AI summary
Embodiments described herein provide a cross-lingual sentence alignment framework that is trained only on rich-resource language pairs. To obtain an accurate aligner, a pretrained multi-lingual language model is used, and a classifier is trained on parallel data from rich-resource language pairs. This trained classifier may then be used for cross-lingual transfer with low-resource languages.