Abstract: This project deals with genre-based controllable movie script generation. We fine-tune two language models - GPT-2 and BART on the IMSDB movie script dataset using special genre tags to delineate the styles of the script. By learning the embeddings for these genre tokens we generate novel scripts for unique combinations of genres during inference by using these genre tokens as the input. To evaluate our model we fine-tune a BERT classifier, to identify the most likely genre(s) for a given script. We also explore other unsupervised methods, inspired from evaluating style transfer techniques, to verify whether our controllable model is actually generating scripts within the specified genre. Qualitatively our proposed method proved to be better than our baselines, even though quantitative results didn’t completely support this. However the evaluation techniques we propose could be beneficial for future avenues of research

Read the full report: Final_Report_NLP.pdf

Example of scripts generated with the prompt " He hits a button" (with different Genres)

python train_scripts.py

Note: Edit argument parameters if you want to use non-default hyper-parameters.

1. The following command fine-tunes the chosen model on SWAG. The available models are GPT-2. (BERT and RoBERTa should work but I haven't tested them yet)

python generate_scripts.py \
--text "text prompt to initialize generation" \
--genres "<Genre1> <Genre2> <Genre3> ....." \
--checkpoint './models/<checkpoint_folder>'
--learning_rate 3e-4 

Note: For initializing genres make sure you follow the same format as shown above, ex: " "

Our movie scripts dataset, model files, intermediate pickle files, and generated examples can be downloaded here:

• Controlled Movie Scripts Generation Data

./Datasets/

• includes raw and preprocessed IMSDB data
• fast_* files contain the chunked movie scripts to (300 and 512 token chunks)
  • They are in the format required by FastBert library
  • They are useful when running StyleClassifier_BERT.py or notebooks/StyleClassifier_BERT.ipynb Download genre.csv file and store it in ./data/
• IMSDB_movies_dataset.csv contains ALL the movies data
• If downloaded should be stored in HOME/data/ (HOME refers to project home wherever the repo is cloned)

./Models/

• Contain model files for GPT2, BART, BERT, and N-Grams
• Also contain other misc pickle files for GPT and tfidf word vectors tSNE visualizations
• If downloaded should be stored in **HOME/models/<ngram_models>
  • if downloading ngram models, add add ngram_models to the path

./Generations/

• generated text is can be found in this repo (HOME/data/) but can also be donwloaded from gDrive.

./EvaluationStatistics/

• These are also available here (HOME/data) but can also be downloaded from gDrive/

• OpenAi/GPT2 follow MIT license, huggingface/pytorch-pretrained-BERT is Apache license.
• We follow MIT license with original GPT2 repository