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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

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