Developing Conversational Data and Detection of Conversational Humor in Telugu

Vaishnavi Pamulapati, Radhika Mamidi


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.
Anthology ID:
2021.codi-main.2
Volume:
Proceedings of the 2nd Workshop on Computational Approaches to Discourse
Month:
November
Year:
2021
Address:
Punta Cana, Dominican Republic and Online
Venues:
CODI | CRAC | EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
12–19
Language:
URL:
https://aclanthology.org/2021.codi-main.2
DOI:
10.18653/v1/2021.codi-main.2
Bibkey:
Cite (ACL):
Vaishnavi Pamulapati and Radhika Mamidi. 2021. Developing Conversational Data and Detection of Conversational Humor in Telugu. In Proceedings of the 2nd Workshop on Computational Approaches to Discourse, pages 12–19, Punta Cana, Dominican Republic and Online. Association for Computational Linguistics.
Cite (Informal):
Developing Conversational Data and Detection of Conversational Humor in Telugu (Pamulapati & Mamidi, CODI 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.codi-main.2.pdf