
doi: 10.5281/zenodo.21591703 , 10.5281/zenodo.21679687 , 10.5281/zenodo.20788942 , 10.5281/zenodo.21591704 , 10.5281/zenodo.21318705 , 10.5281/zenodo.21318706 , 10.5281/zenodo.21261339 , 10.5281/zenodo.21261338 , 10.5281/zenodo.20788943 , 10.5281/zenodo.21232042 , 10.5281/zenodo.21232041 , 10.5281/zenodo.21679688
doi: 10.5281/zenodo.21591703 , 10.5281/zenodo.21679687 , 10.5281/zenodo.20788942 , 10.5281/zenodo.21591704 , 10.5281/zenodo.21318705 , 10.5281/zenodo.21318706 , 10.5281/zenodo.21261339 , 10.5281/zenodo.21261338 , 10.5281/zenodo.20788943 , 10.5281/zenodo.21232042 , 10.5281/zenodo.21232041 , 10.5281/zenodo.21679688
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: Does intermediate-task training on multilingual datasets (e.g., XNLI) instead of English-only datasets improve zero-shot cross-lingual transfer performance on XTREME-R, assessed by F1 score comparisons? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.
This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 8.5/10.
mT5, English-only, training, accuracy, XGLUE, datasets, enhance, XNLI, intermediate-task, improve, extent, zero-shot, intermediate, impact, cross-lingual, instead, use, tasks, multilingual, XLM-R, transfer
mT5, English-only, training, accuracy, XGLUE, datasets, enhance, XNLI, intermediate-task, improve, extent, zero-shot, intermediate, impact, cross-lingual, instead, use, tasks, multilingual, XLM-R, transfer
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