Intermediate Task Selection Strategies for Zero-Shot Cross-Lingual Transfer in XTREME-R

Multilingual pre-trained contextual embedding models (Devlin et al., 2019) have achieved impressive performance on zero-shot cross-lingual transfer tasks. Finding the most effective fine-tuning strategy to fine-tune these models on high-resource languages so that it transfers well to the zero-shot languages is a non-trivial task. In this paper, we propose a novel meta-optimizer to soft-select which layers of the pre-trained model to freeze during fine-tuning. We train the meta-optimizer by simulating the zero-shot transfer scenario. Results on cross-lingual natural language inference show that

Research goal: How do different intermediate task selection strategies (e.g., task diversity, task similarity to target) impact the zero-shot cross-lingual transfer performance on XTREME-R tasks, measured by accuracy and inference latency?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.