PO-EMO generation

This repository contains the code for the experiment we did in our term paper 'Modeling and Generation of Emotion and Emotion Arcsin Prose and Poetry'. It is largely based on the code of Modeling Protagonist Emotions for Emotion-Aware Storytelling Faeze Brahman, and Snigdha Chaturvedi. EMNLP 2020 and uses the PO-EMO dataset.

Data files includes:

1. [train/test/dev]_x1.txt: titles
2. [train/test/dev]_x4.txt: stanzas
3. [train/test/dev]_mapped.txt: stanza emotions

Code

• The code depends on Python 3.6, a specific choice of outdated packages (req.txt), as well as a patched version of Texar. Please install the version under third_party/texar. Follow the installation instructions in the README there.
• Download gpt-2-M from here and put it in gpt2_pretrained_models/ folder.
• Use prepare_poemo.py to process PO-EMO's tsv format
• Use translate.py to translate the german dataset into english
• Use cat to concatenate the english and german dataset
• Use split.py for the train/test/dev split
• Use prepare_data.py to preprocess the data and transform them into TFRecord format. An example command is (please see the code for more config options).

python prepare_data.py --data_dir=data

• Run run_emosup.sh for training/testing the model. (please see config files for more config options.)

Interactive Generation

First, download the pretrained model from here and untar it:

tar -xvzf model.tar.gz

Then run following command to interactively generate emotion-aware stories:

sh run_interactive.sh

Running that, it will ask you to first enter a Title, and then a sequence of emotions separated by space. For example: joy sadness sadness

The code is adapted from Counterfactual Story Generation.

Reference

This experiment is largely based on this work:

@inproceedings{brahman-chaturvedi-2020-modeling,
    title = "Modeling Protagonist Emotions for Emotion-Aware Storytelling",
    author = "Brahman, Faeze  and
      Chaturvedi, Snigdha",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.426",
    pages = "5277--5294"
}