Contrastive-Adversarial Fine-Tuning for Zero-Shot Cross-Lingual Performance in XLM-R

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: How does the combination of contrastive learning and adversarial training during XLM-R fine-tuning impact zero-shot cross-lingual performance on XTREME-R when evaluated with macro-F1 score, compared to vanilla adversarial training, and does this improvement scale with the number of languages included in training?

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