Summary of Revealing the Impact Of Synthetic Native Samples and Multi-tasking Strategies in Hindi-english Code-mixed Humour and Sarcasm Detection, by Debajyoti Mazumder et al.
Revealing the impact of synthetic native samples and multi-tasking strategies in Hindi-English code-mixed humour and sarcasm detection
by Debajyoti Mazumder, Aakash Kumar, Jasabanta Patro
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents an investigation into improving humour and sarcasm detection in Hindi-English code-mixed text through various strategies. The researchers experimented with three approaches: native sample mixing, multi-task learning (MTL), and prompting very large multilingual language models (VMLMs). Native sample mixing showed improved performance for both humour and sarcasm detection, while MTL training boosted performance to a higher extent. VMLM prompting did not outperform the other methods. The study also includes ablation studies and error analysis to identify areas for future improvement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making computers better at understanding funny jokes and sarcastic comments in mixed Hindi-English language. They tried different ways to do this, like adding more examples of plain text and doing multiple tasks at once. They found that adding more examples helped a bit, but it was the second method that really made a big difference. The third way didn’t work as well. The researchers also looked into what went wrong and where they can improve. |
Keywords
» Artificial intelligence » Multi task » Prompting
