Domain Alignment Impact on Zero-Shot Cross-Lingual Transfer in XTREME Benchmark

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on non-English target tasks. Using nine intermediate language-understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on the XTREME benchmark. We see large improvements from intermediate training on the BUCC and Tatoeba sentence retrieval tas

Research goal: How does the domain alignment between intermediate tasks (e.g., machine translation vs. NER) and target tasks affect zero-shot cross-lingual transfer performance for low-resource languages in the XTREME benchmark, measured by accuracy and inference latency trade-offs?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.2/10.