Question: How much better was results of CamemBERT than previous results on these tasks?
Answer: 2.36 point increase in the F1 score with respect to the best SEM architecture, on the TRANSLATE-TRAIN setting (81.2 vs. 80.2 for XLM), lags behind models trained on the original English training set in the TRANSLATE-TEST setting, 81.2 vs. 82.91 for RoBERTa, For POS tagging, we observe error reductions of respectively 0.71% for GSD, 0.81% for Sequoia, 0.7% for Spoken and 0.28% for ParTUT, For parsing, we observe error reductions in LAS of 2.96% for GSD, 3.33% for Sequoia, 1.70% for Spoken and 1.65% for ParTUT