Cross-lingual Transfer Performance Variation with Intermediate Task Training and Labeled Data Ratios

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 impact of intermediate task training on zero-shot cross-lingual transfer performance vary when using different ratios of labeled examples (e.g., 0.1%, 1%, 5%) in the target language, measured by accuracy degradation thresholds on XTREME-R?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10.