Konkani Figurative Language Corpus (Idioms + Metaphors) 📚

A dataset for idiom and metaphor classification in low-resource Konkani.

🚀 Overview

This dataset extends the Konidioms Corpus (Shaikh et al., 2024) by adding metaphor annotations. It supports binary classification for both idioms and metaphors in Konkani, a multi-script language spoken by approximately 2.5 million people.

📊 Dataset Description

Language: Konkani (Devanagari script)
Tasks:
- Idiom Detection (Yes/No)
- Metaphor Detection (Yes/No)
Format: CSV with the following columns:
- id: Sentence identifier
- sentence: The Konkani sentence
- idiom: Yes or No
- metaphor: Yes or No
- split: train or test
Size:
- 6,520 idiom-annotated sentences
- 500 metaphor-annotated sentences
Splits:
- ~80% training
- ~20% testing

🎯 Motivation

Konkani is a low-resource language with significant dialect and script variation. Figurative language, particularly metaphors, remains understudied. This dataset allows exploration of idioms and metaphors within a single corpus and supports efficient modeling efforts in underrepresented languages.

🧰 Baseline Model

From the paper: Pruning for Performance: Efficient Idiom and Metaphor Classification in Low-Resource Konkani Using mBERT

Model: mBERT + BiLSTM
Optimization: Gradient-based attention head pruning
Result: Comparable performance to full mBERT with fewer parameters

🧪 Example Use Cases

Figurative language analysis in Indic languages
Efficient multilingual transformer training
Evaluation of pruning strategies for model compression
Cross-lingual transfer learning in low-resource contexts

📥 Usage

# Clone the repository (if applicable)
git clone <https://github.com/t1modo/Algoverse_KonkaniNLP>
cd konkani-figurative-corpus

# Load the dataset in Python
import pandas as pd
df = pd.read_csv("konidioms_metaphors.csv")
print(df.head())

📚 Citation

If you use this dataset, please cite:

@article{do2025pruning,
  title={Pruning for Performance: Efficient Idiom and Metaphor Classification in Low-Resource Konkani Using mBERT},
  author={Do, Timothy and Saran, Pranav and Poojary, Harshita and Prabhu, Pranav and O’Brien, Sean and Sharma, Vasu and Zhu, Kevin},
  journal={arXiv preprint arXiv:2506.02005},
  year={2025}
}

📝 License

Released under the CC-BY 4.0 License.

⚠️ Limitations

Limited metaphor annotations (500 examples)
Only Devanagari script supported
Domain-specific language bias possible

🙌 Contributions

Contributions welcome! Please open an issue or pull request for improvements or additional data.