Scaling Intermediate Task Diversity in Zero-Shot Cross-Lingual Transfer on XTREME-R

In zero-shot cross-lingual transfer, a supervised NLP task trained on a corpus in one language is directly applicable to another language without any additional training. A source of cross-lingual transfer can be as straightforward as lexical overlap between languages (e.g., use of the same scripts, shared subwords) that naturally forces text embeddings to occupy a similar representation space. Recently introduced cross-lingual language model (XLM) pretraining brings out neural parameter sharing in Transformer-style networks as the most important factor for the transfer. In this paper, we aim

Research goal: How does the scaling of intermediate task diversity (e.g., 3 vs. 9 tasks) impact zero-shot cross-lingual transfer performance on XTREME-R across different language families?

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