Hybrid Batch Training for Adversarial Robustness in Zero-Shot Cross-Lingual Retrieval on Low-Resource XTYNTEGRA Pairs

Abstract

Abstract: Pre-trained multilingual language encoders, such as multilingual BERT and XLM-R, show great potential for zero-shot cross-lingual transfer. However, these multilingual encoders do not precisely align words and phrases across languages. Especially, learning alignments in the multilingual embedding space usually requires sentence-level or word-level parallel corpora, which are expensive to be obtained for low-resource languages. An alternative is to make the multilingual encoders more robust; when fine-tuning the encoder using downstream task, we train the encoder to tolerate noise in the contex

Research Question

How does hybrid batch training impact the adversarial robustness of zero-shot cross-lingual retrieval models on low-resource language pairs in the XTYNTEGRA benchmark?

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