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Please leave a star if you use our code

The input for our experiments are located in the folder data/non-sarcastic.txt

The generation for our paper is a three staged pipeline process

We convert a non-sarcastic utterance to a sarcastic

R-3

conda create --name R3 python=3.6

conda activate R3

#point your LD_LIBRARY_PATH to your miniconda or anaconda library

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/nas/home/tuhinc/miniconda3/lib/

Clone this repo.

  • cd SarcasmGeneration-ACL2020

  • install nltk

  • To just reverse the valence of the input (REVERSE)

    • Run python reverse.py $input
    • This will print the output in the console

  • To retrieve the commonsense keyword associated with the input you need to do the below(RETRIEVE)

    - Download a chunk of the retreival corpus from the timestamp of the experiment from the link below and put inside data folder (https://drive.google.com/file/d/1cX-iy58B0F1K2nVD5sMiSIxd-8lFx7ok/view?usp=sharing)

    • cd comet-commonsense

      To run the setup scripts to acquire the pretrained model files from OpenAI, as well as the ATOMIC and ConceptNet datasets

      bash scripts/setup/get_atomic_data.sh
bash scripts/setup/get_conceptnet_data.sh
bash scripts/setup/get_model_files.sh

      Then install dependencies (assuming you already have Python 3.6 ):

      pip install torch==1.3.1
pip install tensorflow
pip install ftfy==5.1
conda install -c conda-forge spacy
python -m spacy download en
pip install tensorboardX
pip install tqdm
pip install pandas
pip install ipython
pip install inflect
pip install pattern
pip install pyyaml==5.1

      Making the Data Loaders

      Run the following scripts to pre-initialize a data loader for ATOMIC or ConceptNet:

      python scripts/data/make_atomic_data_loader.py
python scripts/data/make_conceptnet_data_loader.py

      Download pretrained COMET

      First, download the pretrained models from the following link:

      https://drive.google.com/open?id=1FccEsYPUHnjzmX-Y5vjCBeyRt1pLo8FB

      Then untar the file:

      tar -xvzf pretrained_models.tar.gz

Make sure your directory resembles this https://github.com/tuhinjubcse/SarcasmGeneration-ACL2020/blob/master/comet-commonsense/directory.md

  • Finally to generate sarcasm , pick an utterance from data/non-sarcastic.txt and run the following command which inclueds REVERSE , RETRIEVE , RANK together

    • Run python generate_sarcasm.py $input
    • This will print the output in the console

Email me at tc2896@columbia.edu for any problems/doubts. Further you can raise issues on github, or suggest improvements.

If you use code or data please cite us

      @inproceedings{chakrabarty-etal-2020-r,
   title = "{R}{\^{}}3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge",
   author = "Chakrabarty, Tuhin  and
     Ghosh, Debanjan  and
     Muresan, Smaranda  and
     Peng, Nanyun",
   booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
   month = jul,
   year = "2020",
   address = "Online",
   publisher = "Association for Computational Linguistics",
   url = "https://www.aclweb.org/anthology/2020.acl-main.711",
   pages = "7976--7986",
}