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license: apache-2.0
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# Konkani Figurative Language Corpus (Idioms + Metaphors) 📚
**A dataset for idiom and metaphor classification in low-resource Konkani.**
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## 🚀 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.
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## 📊 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
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## 🎯 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.
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## 🧰 Baseline Model
From the paper: [Pruning for Performance: Efficient Idiom and Metaphor Classification in Low-Resource Konkani Using mBERT](https://arxiv.org/abs/2506.02005)
- **Model**: mBERT + BiLSTM
- **Optimization**: Gradient-based attention head pruning
- **Result**: Comparable performance to full mBERT with fewer parameters
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## 🧪 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
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## 📚 Citation
If you use this dataset, please cite:
```bibtex
@misc{do2025pruningperformanceefficientidiom,
title={Pruning for Performance: Efficient Idiom and Metaphor Classification in Low-Resource Konkani Using mBERT},
author={Timothy Do and Pranav Saran and Harshita Poojary and Pranav Prabhu and Sean O'Brien and Vasu Sharma and Kevin Zhu},
year={2025},
eprint={2506.02005},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2506.02005},
}
```
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## 📝 License
Released under the [CC-BY 4.0 License](https://creativecommons.org/licenses/by/4.0/).
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## ⚠️ Limitations
- Limited metaphor annotations (500 examples)
- Only Devanagari script supported
- Domain-specific language bias possible
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## 🙌 Contributions
Contributions welcome! Please open an issue or pull request for improvements or additional data.
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