BIHEADS: Multi-Task BiLSTM+BiGRU for Egyptian Arabic
BIHEADS is a recurrent neural network ensemble designed to provide a complementary perspective to transformer-based models for Egyptian Arabic. It uses FastText embeddings and a parallel architecture to capture temporal nuances in dialectal speech.
Model Details
- Architecture: Parallel BiLSTM + BiGRU shared backbone
- Embeddings: FastText
facebook/fasttext-arz-vectors(300-dim) - Hidden Size: 256 per direction
- Layers: 2-layer LSTM, 2-layer GRU
Task Heads & Labels
- Emotion (8 classes):
none,anger,joy,sadness,love,sympathy,surprise,fear - Sentiment (3 classes):
negative,neutral,positive - Sarcasm (2 classes):
not sarcastic,sarcastic
Training Data
The model utilizes the same Egyptian-filtered training splits as MASRIHEADS to ensure feature alignment for downstream ensemble learners.
Performance (Test Set F1)
| Task | Macro-F1 |
|---|---|
| Sarcasm | 0.6293 |
| Sentiment | 0.6284 |
| Emotion | 0.5514 |
| Mean | 0.6030 |