Performance Comparison of Dual-Encoder and Transformer Models on Zero-Shot Cross-Lingual NLI Tasks

Abstract

Abstract: The introduction of pretrained cross-lingual language models brought decisive improvements to multilingual NLP tasks. However, the lack of labelled task data necessitates a variety of methods aiming to close the gap to high-resource languages. Zero-shot methods in particular, often use translated task data as a training signal to bridge the performance gap between the source and target language(s). We introduce XeroAlign, a simple method for task-specific alignment of cross-lingual pretrained transformers such as XLM-R. XeroAlign uses translated task data to encourage the model to generate sim

Research Question

How does the performance of dual-encoder models trained on XTREME-R compare to transformer-based models like mBERT or XLM-R on zero-shot cross-lingual natural language inference tasks, measured by accuracy and F1 scores?

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