MASRIHEAD-Unified: Cascading Multi-Task Egyptian NLU
Global Average Macro F1: 0.7170
Language: Egyptian Arabic (ar-EG)
Base Architecture: T0KII/MASRIBERTV4 + FastText + BiLSTM/BiGRU
License: CC-BY-NC-4.0
Overview
MASRIHEAD-Unified is a highly specialized, multi-task Natural Language Understanding (NLU) engine designed natively for the Egyptian Arabic dialect. Developed for the KALAMNA conversational AI ecosystem, it executes three simultaneous downstream tasks: Sarcasm Detection, Sentiment Analysis, and Emotion Classification.
Unlike standard linear multi-task models, this network utilizes a Cascading Neural Architecture (Mathematical Intertwining) that explicitly routes upstream pragmatic predictions (Sarcasm) into downstream semantic heads (Sentiment and Emotion) to mimic human linguistic processing.
🧠 Architectural Details
1. Dual-Encoder Feature Fusion (1792-Dimensions)
The model processes text through two entirely orthogonal feature extractors before fusing them into a dense representation:
- Contextual Semantics (Transformer):
T0KII/MASRIBERTV4 (110M parameters) handles whole-sentence semantic comprehension.
- Morphological Syntax (RNN):
facebook/fasttext-arz-vectors (300d) feeds into a parallel BiLSTM (2x256) and BiGRU (2x256) to capture street-level syntax, neologisms, and heavy Franco-Arabic morphological variations.
2. The Cascading Multi-Task Routing
To combat the high ambiguity of Egyptian dialects, the classification heads are mathematically intertwined.
- Sarcasm Head (2-Class): Evaluates the base 1792-dimension fusion layer.
- Sentiment Head (3-Class): Ingests the 1792-dimension fusion layer + the 2 Sarcasm logits (1794d).
- Emotion Head (4-Class): Ingests the base layer + Sarcasm logits + Sentiment logits (1797d).
📊 Performance Benchmarks
The model was trained dynamically via a sparse-matrix CrossEntropyLoss function (ignore_index=-100) on the T0KII/MASRISET-V4-DOWNSTREAM-SPLIT dataset (~121,000 rows).
| Task | Output Classes | Macro F1 Score |
|---|
| Sentiment | Negative(0), Neutral(1), Positive(2) | 0.7822 |
| Sarcasm | Literal(0), Sarcastic(1) | 0.7342 |
| Emotion | Anger(0), Joy(1), Neutral(2), Sadness(3) | 0.6347 |
| Global Average | - | 0.7170 |
