Bubbli/XLM-R-Sarcasm-MockPoliteness-Detection

XLM-RoBERTa with Multi-Task Learning for Sarcasm and Mock Politeness Detection

Model Description

This project fine-tunes XLM-RoBERTa for detecting sarcasm and mock politeness in Filipino (English, Tagalog, or code-mixed (Taglish)) faculty evaluation texts.

Two models are included:

• MTL model → sarcasm detection (main task) + mock politeness detection (auxiliary task)
• STL model → sarcasm detection only

The models are packaged into a desktop app (Tkinter + Python) for easy testing.

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Intended Uses & Limitations

Intended Use

• Demonstrating multi-task learning in NLP
• Exploring sarcasm and politeness detection in Taglish text
• Academic/research purposes only

Limitations

• Trained on a domain-specific dataset (faculty evaluations)
• May not generalize well outside Taglish or academic settings
• Predictions are not guaranteed to be accurate for all contexts

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How to Use

1. Download the XLM-R folder from this repository.
2. Inside the folder, locate and open: XLM-R/XLM-R.exe
3. Use the GUI to input text or upload a .csv file (see included INPUT_SAMPLE.csv).
4. The app will output predictions for sarcasm (and mock politeness if using MTL).

(No coding required — the .exe is standalone on Windows.)

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Training Data

• Collected faculty evaluation texts written in Filipino (English, Tagalog, or code-mixed (Taglish))
• Annotated for sarcasm and mock politeness

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Evaluation

• Compared Single-Task (STL) vs Multi-Task (MTL)
• Metrics: accuracy, precision, recall, F1