Zero-Shot Cross-Lingual Transfer via Intermediate-Task Training in Non-English High-Resource Languages Across Families

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 zero-shot cross-lingual transfer performance vary when using intermediate-task training in non-English high-resource languages (e.g., Spanish, French, German) across multiple language families in benchmarks like XTREME-R and XTREME-RU?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.7/10.