Cross-lingual Transfer Interpretation for NLI Consistency
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Solution Overview
Problem
Cross-lingual models face challenges in understanding and performing natural language inference tasks across languages due to poor alignment of feature attributions and semantic similarity, leading to fluctuating performance across languages for downstream tasks.
Innovation Solution
The method involves cross-lingual transfer interpretation, which includes source-to-target language translation, feature importance extraction using integrated gradients, and cross-lingual feature alignment based on semantic similarity, measured by cosine similarity, to align tokens in both premise and hypothesis pairs across languages, and a qualitative analysis to compare importance scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cross-lingual models are trained on source language data and applied to target language tasks, then the model can perform natural language inference across languages, but the feature attributions and semantic similarity are poorly aligned leading to fluctuating performance
Solution Approach 1:
The patent introduces an intermediary alignment mechanism that maps feature attributions from the source language to the target language through semantic similarity. This intermediary layer reconciles the mismatch between source language model predictions and target language semantic representations, enabling consistent cross-lingual transfer without direct retraining.
2Measurement precision
If feature importance extraction is performed using integrated gradients, then the attribution of each input feature can be measured, but the attributions cannot be directly compared across different languages due to lack of alignment
Solution Approach 1:
The patent transforms the parameter space by projecting feature attributions from different languages into a unified semantic space. By changing the representation parameters through semantic similarity mapping, attributions from source and target languages become comparable while preserving their individual measurement precision.
3Reliability
If tokens are aligned across languages based on semantic similarity, then cross-lingual feature alignment can be achieved, but the computational complexity increases due to comparing token embeddings across language spaces
Solution Approach 1:
The patent creates a universal semantic space that serves multiple functions: it represents tokens from different languages, enables similarity comparison, and provides a common reference frame for alignment. This multi-functional space reduces computational complexity by eliminating the need for separate alignment mechanisms for each language pair.
Data Source
AI summary
Systems and methods are provided for Cross-lingual Transfer Interpretation (CTI). The method includes receiving text corpus data including premise-hypothesis pairs with a relationship label in a source language, and conducting a source to target language translation. The method further includes performing a feature importance extraction, where an integrated gradient is applied to assign an importance score to each input feature, and performing a cross-lingual feature alignment, where tokens in the source language are aligned with tokens in the target language for both the premise and the hypothesis based on semantic similarity. The method further includes performing a qualitative analysis, where the importance score of each token can be compared between the source language and the target language according to a feature alignment result.