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By surpassing the baseline performance, we believe that our work can be a stepping stone for deep learning implementation in argumentation mining field.
The self-training based approach STEOD still falls below the supervised baseline for experiments over 1-to-50, but the co-training based approach CTEOD is surpassing the baseline for all experiments.
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Results: median processing time increased immediately post-implementation and then returned to, and surpassed, the baseline level over 10 months.
On UKBench, 27 descriptors with varying codebooks surpassed the baseline MAP score of 0.8159.
Overall, 10 out of the 16 proposed SIMPLE descriptors managed to surpass the baseline experiment in this collection.
They finally reached 74.3%% recall which significantly surpassed the baseline's recall (3.7 %).
The variants CTEPD and STEPD, which add only positive instances and distribute them, surpassed the baseline for all experiments.
Overall, without pretrain step, the neural network can only surpass the baseline on F1 and micro average.
No significant difference in performance between CTEOD and CTEPD was observed, but STEPD out-performed STEOD and surpassed the baseline in all experiments.
This diversity, measured by the mean number of pairwise differences (MNPDs) between samples, although not statistically significant, could surpass the baseline value calculated for the tested population, as observed in three individuals of sub-haplogroup E3b1* xE3b1a, c1) with MNPDs of 4.0 ± 2.7 against a population baseline of 2.8 ± 1.5.
As can be seen from the table, for lower degrees of imbalance (1-to-5 to 1-to-40), semi-supervised ensembles are considerably surpassing the supervised baselines.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com