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.

1. Sarcasm Head (2-Class): Evaluates the base 1792-dimension fusion layer.
2. Sentiment Head (3-Class): Ingests the 1792-dimension fusion layer + the 2 Sarcasm logits (1794d).
3. 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).