Graph Encoder for Cross-Lingual Zero-Shot Transfer

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Solution Overview

Problem

Cross-lingual zero-shot transfer in Natural Language Processing (NLP) faces challenges due to limited labels in low-resource languages, leading to poor performance in tasks like syntactic tasks, as existing methods struggle to effectively transfer knowledge from high-resource languages to low-resource languages.

Innovation Solution

A Graph Convolutional Network (GCN)-based approach is employed, where a source corpus and a target corpus are tokenized and transformed into dependency parsing trees, then input into a graph encoder pretrained on a translation language modeling task, followed by fine-tuning the graph encoder and downstream network for specific NLP tasks like Named Entity Recognition.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If existing cross-lingual transfer methods are used, then high-resource languages can be processed effectively, but low-resource languages show poor performance especially in syntactic tasks

Engineering Contradiction:

Improveperformance on low-resource languagesVSAvoidapplicability to syntactic tasks

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The patent introduces dependency parsing trees as an intermediary representation that bridges source and target languages. These trees serve as a language-agnostic intermediate form that captures syntactic structure, allowing knowledge transfer from high-resource to low-resource languages while maintaining syntactic task performance. The parsing trees act as a mediator that decouples the transfer process from language-specific surface forms.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent transforms linguistic data from sequential token representations to tree-structured dependency representations, changing the structural parameter of the input data. This transformation enables the model to capture syntactic relationships more effectively and improves transfer performance on syntactic tasks by representing language data in a form that highlights structural similarities across languages.

Inventive Principle:

Principle #35Parameter changes

2Reliability

If more training labels are collected for low-resource languages, then model performance improves, but data collection and annotation costs increase

Engineering Contradiction:

Improvemodel performance on target corpusVSAvoidamount of labeled data required

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The patent performs preliminary action by pre-training the graph encoder on source language dependency parsing trees before fine-tuning on the target corpus. This pre-training phase allows the model to learn universal syntactic representations from high-resource languages, which are then transferred to low-resource languages with minimal labeled data, reducing the amount of target language annotations needed.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The model leverages the structural similarities inherent in human language to perform self-service knowledge transfer. By learning from the syntactic structures of high-resource languages and applying this knowledge to low-resource languages through the dependency parsing framework, the system serves itself by automatically transferring linguistic knowledge without requiring extensive manual annotation of target language data.

Inventive Principle:

Principle #25Self-service

3Measurement precision

If traditional NLP models are used, then English and high-resource languages achieve good results, but cross-lingual zero-shot transfer performance deteriorates

Engineering Contradiction:

Improveaccuracy on high-resource languagesVSAvoidzero-shot transfer capability

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The patent creates a universal representation framework using dependency parsing trees that can handle multiple languages and tasks uniformly. The graph encoder is designed to process linguistic structures from any language in a language-agnostic manner, enabling the same model to achieve high accuracy on both high-resource languages and enable zero-shot transfer to low-resource languages without task-specific adaptations.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12050870B2Cross-lingual zero-shot transfer via semantic and synthetic representation learning

Publication Date: 2024.07.30 NEC CORP

AI summary

A computer-implemented method is provided for cross-lingual transfer. The method includes randomly masking a source corpus and a target corpus to obtain a masked source corpus and a masked target corpus. The method further includes tokenizing, by pretrained Natural Language Processing (NLP) models, the masked source corpus and the masked target corpus to obtain source tokens and target tokens. The method also includes transforming the source tokens and the target tokens into a source dependency parsing tree and a target dependency parsing tree. The method additionally includes inputting the source dependency parsing tree and the target dependency parsing tree into a graph encoder pretrained on a translation language modeling task to extract common language information for transfer. The method further includes fine-tuning the graph encoder and a down-stream network for a specific NLP down-stream task.