@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.",
}
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%0 Conference Proceedings
%T Developing Conversational Data and Detection of Conversational Humor in Telugu
%A Pamulapati, Vaishnavi
%A Mamidi, Radhika
%Y Braud, Chloé
%Y Hardmeier, Christian
%Y Li, Junyi Jessy
%Y Louis, Annie
%Y Strube, Michael
%Y Zeldes, Amir
%S Proceedings of the 2nd Workshop on Computational Approaches to Discourse
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic and Online
%F pamulapati-mamidi-2021-developing
%X 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.
%R 10.18653/v1/2021.codi-main.2
%U https://aclanthology.org/2021.codi-main.2
%U https://doi.org/10.18653/v1/2021.codi-main.2
%P 12-19
Markdown (Informal)
[Developing Conversational Data and Detection of Conversational Humor in Telugu](https://aclanthology.org/2021.codi-main.2) (Pamulapati & Mamidi, CODI 2021)
ACL