Question: How many epochs does pretraining set to？
Evidence: Using GANs, we can train generative models in a two-player game setting between a discriminator and a generator, where the discriminator (a binary classifier) learns to distinguish between real and fake data samples and the generator tries to fool the discriminator by generating authentic and high quality output BIBREF16. GANs have shown to be successful in image generation tasks BIBREF17 and recently, some progress has been observed in text generation BIBREF13, BIBREF12, BIBREF15. Our generator is a language model trained using backpropagation through time BIBREF18. During the pre-training phase we optimize for MLE and during the GAN training phase, we optimize on the creativity reward from the discriminator. The discriminator's encoder has the same architecture as the generator encoder module with the addition of a pooled decoder layer. The decoder contains 3 $[Dense Batch Normalization,ReLU]$ blocks and an addtional $Sigmoid$ layer. The discriminator decoder takes the hidden state at the last time step of a sequence concatenated with both the max-pooled and mean-pooled representation of the hidden states BIBREF19 and outputs a number in the range $[0,1]$. The difficulty of using GANs in text generation comes from the discrete nature of text, making the model non-differentiable hence, we update parameters for the generator model with policy gradients as described in Yu BIBREF15.
We utilize AWD-LSTM BIBREF20 and TransformerXL BIBREF21 based language models. For model hyperparameters please to refer to Supplementary Section Table TABREF5. We use Adam optimizer BIBREF22 with $\beta 1= 0.7$ and $\beta 2= 0.8$ similar to BIBREF19 and use a batch size of 50. Other practices for LM training were the same as BIBREF21 and BIBREF20 for Transformer-XL and AWD-LSTM respectively. We refer to our proposed GAN as Creative-GAN and compare it to a baseline (a language model equivalent to our pre-trained generator) and a GumbelGAN model BIBREF14 across all proposed datasets. We use three creative English datasets with distinct linguistic characteristics: (1) A corpus of 740 classical and contemporary English poems, (2) a corpus of 14950 metaphor sentences retrieved from a metaphor database website and (3) a corpus of 1500 song lyrics ranging across genres. The mix of linguistic styles within this corpus offers the potential for interesting variation during the generation phase. We use the same pre-processing as in earlier work BIBREF19, BIBREF23. We reserve 10% of our data for test set and another 10% for our validation set.
We first pre-train our generator on the Gutenberg dataset BIBREF24 for 20 epochs and then fine-tune BIBREF19 them to our target datasets with a language modeling objective. The discriminator's encoder is initialized to the same weights as our fine-tuned language model. Once we have our fine-tuned encoders for each target dataset, we train in an adversarial manner. The discriminator objective here is to score the quality of the creative text. The discriminator is trained for 3 iterations for every iteration of the generator, a practice seen in previous work BIBREF25. Creative-GAN relies on using the reward from the discriminator BIBREF12, BIBREF15 for backpropagation. We follow a similar training procedure for GumbelGAN. Outputs are generated through sampling over a multinomial distribution for all methods, instead of $argmax$ on the log-likelihood probabilities, as sampling has shown to produce better output quality BIBREF4. Please refer to Supplementary Section Table TABREF6 for training parameters of each dataset and Table TABREF5 for hyperparameters of each encoder. We pick these values after experimentation with our validation set. Training and output generation code can be found online.
We first pre-train our generator on the Gutenberg dataset [25] for 20 epochs and then fine-tune [20] them to our target datasets with a language modeling objective.
Answer: 20 epochs.