Code-Pretrained versus Text-Only Models in Zero-Shot Cross-Lingual Transfer via Intermediate English Language Understanding Tasks

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 choice of intermediate English language understanding tasks (e.g., sentiment analysis vs. natural language inference) impact zero-shot cross-lingual transfer performance on XTREME when comparing code-pretrained models (e.g., CodeBERT) to text-only models (e.g., XLM-RoBERTa)?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.3/10.