@inproceedings{pamulapati-mamidi-2021-developing,
    title = "Developing Conversational Data and Detection of Conversational Humor in {T}elugu",
    author = "Pamulapati, Vaishnavi  and
      Mamidi, Radhika",
    editor = "Braud, Chlo{\'e}  and
      Hardmeier, Christian  and
      Li, Junyi Jessy  and
      Louis, Annie  and
      Strube, Michael  and
      Zeldes, Amir",
    booktitle = "Proceedings of the 2nd Workshop on Computational Approaches to Discourse",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic and Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.codi-main.2",
    doi = "10.18653/v1/2021.codi-main.2",
    pages = "12--19",
    abstract = "In the field of humor research, there has been a recent surge of interest in the sub-domain of Conversational Humor (CH). This study has two main objectives. (a) develop a conversational (humorous and non-humorous) dataset in Telugu. (b) detect CH in the compiled dataset. In this paper, the challenges faced while collecting the data and experiments carried out are elucidated. Transfer learning and non-transfer learning techniques are implemented by utilizing pre-trained models such as FastText word embeddings, BERT language models and Text GCN, which learns the word and document embeddings simultaneously of the corpus given. State-of-the-art results are observed with a 99.3{\%} accuracy and a 98.5{\%} f1 score achieved by BERT.",
}