Computational Efficiency of English vs Multilingual Intermediate-Task Training on Edge Devices for XTREME-R+ Inference

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Original abstract

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 computational efficiency (training time, memory usage) of English intermediate-task training compare to multilingual intermediate-task training when deployed on hardware-constrained edge devices for XTREME-R+ inference? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.2/10.

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