@inproceedings{agarwal-narula-2021-humor-generation,
    title = "Humor Generation and Detection in Code-Mixed {H}indi-{E}nglish",
    author = "Agarwal, Kaustubh  and
      Narula, Rhythm",
    editor = "Djabri, Souhila  and
      Gimadi, Dinara  and
      Mihaylova, Tsvetomila  and
      Nikolova-Koleva, Ivelina",
    booktitle = "Proceedings of the Student Research Workshop Associated with RANLP 2021",
    month = sep,
    year = "2021",
    address = "Online",
    publisher = "INCOMA Ltd.",
    url = "https://preview.aclanthology.org/ingest-emnlp/2021.ranlp-srw.1/",
    pages = "1--6",
    abstract = "Computational humor generation is one of the hardest tasks in natural language generation, especially in code-mixed languages. Existing research has shown that humor generation in English is a promising avenue. However, studies have shown that bilingual speakers often appreciate humor more in code-mixed languages with unexpected transitions and clever word play. In this study, we propose several methods for generating and detecting humor in code-mixed Hindi-English. Of the experimented approaches, an Attention Based Bi-Directional LSTM with converting parts of text on a word2vec embedding gives the best results by generating 74.8{\%} good jokes and IndicBERT used for detecting humor in code-mixed Hindi-English outperforms other humor detection methods with an accuracy of 96.98{\%}."
}