Trade-off between inference efficiency and cross-lingual transfer accuracy in adversarially trained XLM-R-large on PAWS-X

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: What is the trade-off between inference efficiency and zero-shot cross-lingual transfer accuracy when combining adversarial robustness training with intermediate task fine-tuning in XLM-R-large on the PAWS-X benchmark?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.2/10.