Inconsistencies with the paper about self-regulation-employing-a-generative-adversarial-network-to-improve-event-detection HOT 3 OPEN

In fact, I think I understant the code more clearly now but it still not consistent:
The value reported in the article is 0.1^3 (0.001) but the value used in the loss is 0.00001 (0.1^5)
The article doesn't mention applying Ldiff to the gan part (train_op_g) .
Furthermore, since defining the loss as the sum of cross_entropy and cross_entropy_binary is not mentionned in the paper, is it significatively helping the performance ?

from self-regulation-employing-a-generative-adversarial-network-to-improve-event-detection.

In fact, I think I understant the code more clearly now but it still not consistent:
The value reported in the article is 0.1^3 (0.001) but the value used in the loss is 0.00001 (0.1^5)
The article doesn't mention applying Ldiff to the gan part (train_op_g) .
Furthermore, since defining the loss as the sum of cross_entropy and cross_entropy_binary is not mentionned in the paper, is it significatively helping the performance ?

Did you get the reported F score? How did you process the raw ACE2005 data. In the code, it seems the sentences less than 8 words are filtered out. How about testing? If so, does it mean the test was not using all the data?

from self-regulation-employing-a-generative-adversarial-network-to-improve-event-detection.

In fact, I think I understant the code more clearly now but it still not consistent:
The value reported in the article is 0.1^3 (0.001) but the value used in the loss is 0.00001 (0.1^5)
The article doesn't mention applying Ldiff to the gan part (train_op_g) .
Furthermore, since defining the loss as the sum of cross_entropy and cross_entropy_binary is not mentionned in the paper, is it significatively helping the performance ?

Did you get the reported F score? How did you process the raw ACE2005 data. In the code, it seems the sentences less than 8 words are filtered out. How about testing? If so, does it mean the test was not using all the data?
Using the all test data.During testing, it doesn't do padding as its parameter is False.

from self-regulation-employing-a-generative-adversarial-network-to-improve-event-detection.