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Multilingual Intermediate-Task Fine-Tuning for Low-Resource Language Zero-Shot Accuracy in XTREME-R

Domaine:

natural language processing

Type de record:

paper
Créateur:
Ass
Éditeur:
Zenodo
Hôte:avatar
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 multilingual intermediate-task fine-tuning compare to English-only intermediate training in improving zero-shot accuracy on low-resource languages within the XTREME-R benchmark? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.2/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 9.2/10.

Visit

doi.orgzenodo.org

Tasks

transfer learning

Tags

multilingualintermediate-taskfine-tuningEnglish-onlyintermediatetrainingimprovingzero-shot

Licenses

Creative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

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