Question: Which datasets are used?

Evidence:
raw_evidence: 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.
raw_evidence: 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.

Answers:
A corpus of 740 classical and contemporary English poems,  a corpus of 14950 metaphor sentences retrieved from a metaphor database website , a corpus of 1500 song lyrics ranging across genres, Gutenberg dataset 
(1) A corpus of 740 classical and contemporary English poems, (2) a corpus of 14950 metaphor sentences retrieved from a metaphor database website, (3) a corpus of 1500 song lyrics ranging across genres, Gutenberg dataset BIBREF24