Scaling Effects of Multilingual Encoders on Zero-Shot Cross-Lingual Transfer Accuracy in XTREME-R Benchmark

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 scaling of model size in multilingual encoders like XLM-R impact zero-shot cross-lingual transfer accuracy on the XTREME-R benchmark when using contrastive learning objectives versus intermediate task training?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.1/10.