Robustness Training Generalization in Zero-Shot Cross-Lingual Transfer Across Benchmarks

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 goal: Do robustness training techniques for zero-shot cross-lingual transfer generalize to other benchmarks beyond XTREME-R, such as TyDi QA or PAWS-X, and how does the performance scale with model size?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.0/10.