Cross-lingual Regularization for Low-Resource NLP
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Solution Overview
Problem
Deep learning-based natural language processing systems face challenges in handling low-resource languages due to limited data availability, as they often struggle to transfer well to languages with scarce training datasets.
Innovation Solution
The implementation of cross-lingual regularization, which uses training data from another language to improve NLP system performance through transfer learning, by generating a multilingual regularization dataset through translation and training neural networks without explicit alignment of word embeddings, seamlessly scaling for multiple languages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning-based approaches are used for NLP tasks, then state-of-the-art results are achieved, but large amounts of annotated data are required
Solution Approach 1:
The patent introduces a high-resource language as an intermediary medium to transfer knowledge to low-resource languages. By translating training data between languages and using cross-lingual regularization, the system leverages abundant data from high-resource languages to improve performance on low-resource languages without requiring explicit alignment of word embeddings, thus resolving the contradiction between achieving state-of-the-art results and requiring large amounts of annotated data.
2Measurement precision
If systems are trained on one dataset, then performance on that dataset is optimized, but transfer to other datasets is poor
Solution Approach 1:
The patent makes the NLP system universally applicable across multiple languages and datasets by implementing cross-lingual regularization. The system trains on translated data from multiple languages simultaneously, enabling it to generalize across different datasets and languages. This multi-functionality allows the system to maintain optimized performance on specific datasets while also achieving good transferability to other datasets through the shared cross-lingual representation.
3Adaptability or versatility
If explicit alignment of word embeddings is performed for transfer learning, then cross-lingual transfer is improved, but system complexity increases
Solution Approach 1:
The patent extracts and removes the complex explicit alignment process from the transfer learning pipeline. Instead of performing explicit word embedding alignment between languages, the system translates training data into target languages and uses cross-lingual regularization during training. This extraction of the alignment step simplifies the system while maintaining effective cross-lingual transfer capability through the regularization term that enforces consistency across language translations.
Data Source
AI summary
Approaches for cross-lingual regularization for multilingual generalization include a method for training a natural language processing (NLP) deep learning module. The method includes accessing a first dataset having a first training data entry, the first training data entry including one or more natural language input text strings in a first language; translating at least one of the one or more natural language input text strings of the first training data entry from the first language to a second language; creating a second training data entry by starting with the first training data entry and substituting the at least one of the natural language input text strings in the first language with the translation of the at least one of the natural language input text strings in the second language; adding the second training data entry to a second dataset; and training the deep learning module using the second dataset.