Humor Generation and Detection in Code-Mixed Hindi-English

Kaustubh Agarwal, Rhythm Narula


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%.
Anthology ID:
2021.ranlp-srw.1
Volume:
Proceedings of the Student Research Workshop Associated with RANLP 2021
Month:
September
Year:
2021
Address:
Online
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
1–6
Language:
URL:
https://aclanthology.org/2021.ranlp-srw.1
DOI:
Bibkey:
Cite (ACL):
Kaustubh Agarwal and Rhythm Narula. 2021. Humor Generation and Detection in Code-Mixed Hindi-English. In Proceedings of the Student Research Workshop Associated with RANLP 2021, pages 1–6, Online. INCOMA Ltd..
Cite (Informal):
Humor Generation and Detection in Code-Mixed Hindi-English (Agarwal & Narula, RANLP 2021)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2021.ranlp-srw.1.pdf