Apply to my own data

[dberma15]

Hi,
I'm looking to apply this to my own data after reading the paper "Improving Conditional Sequence Generative Adversarial Networks by Stepwise Evaluation". I'm a bit confused in looking at the code, though. Where exactly are the for the input sequence and the ground-truth is read into the code? I see the training and test sequences, but those appear to be the input sequences. I'm not sure where the ground-truth responses are. As a follow up, what is the dev data?

Thanks

[Pascalson]

Hi,

1. The input sequences are the odd lines and the paired ground-truths are the even lines.
2. The dev data is "development set" or "validation set". It is used to early-stop the training.

Hope you find it helpful!

[dberma15]

Thanks so much. That did help. I was wondering if you could explain a bit about how the reward works in this? What are the rewards at each time step and how are they determined?

[Pascalson]

The rewards for the GAN-based models are predicted by the discriminator (D). Each reward can be interpreted as the expected D's score the current sub-sequence will obtain. Then, the generator will tend to generate sequence s that can achieve higher expected D's scores.

[dberma15]

Is the algorithm using teacher forcing when training the decoder?