Rhythm Narula


2021

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Humor Generation and Detection in Code-Mixed Hindi-English
Kaustubh Agarwal | Rhythm Narula
Proceedings of the Student Research Workshop Associated with RANLP 2021

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%.
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