Something about the best parameters

[rohnson1999]

Hello Jason Wei, great paper great idea and I read your paper about Easy Data Augmentation.

I'm trying to implement your experiment result, but I don't know how you find the best alpha and num_aug.

In Figure3 and 4 from the paper, you draw diagrams of different alpha and different num_aug, so how did you choose alpha when you test num_aug, and how did you choose num_aug when you test different alpha?

I checked the code and I find you set
"alpha_sr=0.3, alpha_ri=0.2, alpha_rs=0.1, p_rd=0.15"
in Figure 4, and
'size_data_f1/1_tiny': [16, 16, 16, 16, 16],size_data_f1/2_small': [16, 16, 16, 16, 16],'size_data_f1/3_standard': [8, 8, 8, 8, 4],size_data_f1/4_full': [8, 8, 8, 8, 4]
in Figure 3.

Could you please explain how you set these parameters? thanks !!

[rohnson1999]

Recently I reviewed the code of eda, and here is my finding :)

(1) Fig. 3 on alpha parameter

alpha parameters are searched within [0.05, 0.1, 0.2, 0.3, 0.4, 0.5]
and n_aug = 16 for all datasets if Number_of_sentence=500 and 2,000
n_aug = 4 for PC dataset, n_aug = 8 for rest of datasets if N=5,000 and Full

(2) Fig. 4 on n_aug parameter

n_aug parameters are searched within [0, 2, 4, 8, 16, 32]
and for all datasets, using standard eda method, which sets alpha_sr=0.3, alpha_ri=0.2, alpha_rs=0.1, alpha_rd=0.15, respectively.