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language:
  - en
  - tl
tags:
  - sarcasm-detection
  - mock-politeness
  - multi-task-learning
  - code-mixed
license: mit
base_model:
  - FacebookAI/xlm-roberta-base

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.

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

How to Use

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

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

Training Data

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

Evaluation

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