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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 [email protected] 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",
}