Robustness Analysis of Zero-Shot Cross-Lingual GED Models on Low-Resource Languages

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

What is the impact of adversarial noise robustness on the accuracy of zero-shot cross-lingual GED models when evaluated on the CoNLL-2014 shared task test set for low-resource languages?

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