Arabic Dotless to Dotted Text Conversion Model

This model is designed to convert dotless Arabic text to dotted (vowelized) Arabic text using a sequence-to-sequence (seq2seq) architecture with an attention mechanism. It employs deep learning techniques, specifically Long Short-Term Memory (LSTM) units, to capture the dependencies within the input and output text sequences.

Key Features:

1. Seq2Seq Architecture

The model follows a typical encoder-decoder structure used in many sequence generation tasks.

The encoder processes the dotless Arabic input text.
The decoder generates the vowelized (dotted) output text.

2. Bidirectional LSTM Encoder

The encoder uses a bidirectional LSTM, allowing the model to capture both past and future context in the input text. This improves the model's understanding of the full sequence.

3. Shared Embedding Layer

Both the encoder and decoder share the same embedding layer, which maps input tokens (characters or subwords) into dense vector representations.
This helps the model generalize better by learning shared patterns across the input and output sequences.

4. Attention Mechanism

The attention mechanism allows the decoder to focus on relevant parts of the input sequence at each step, improving the output sequence's accuracy.
It calculates the context vector based on the weighted sum of encoder outputs, which guides the decoding process.

5. LSTM Decoder

The decoder LSTM takes the encoder's final state and the context vector from the attention mechanism to generate the predicted vowelized output sequence.

6. Dense Output Layer

The output layer is a dense layer that generates a probability distribution over the possible output tokens, including diacritics.
The model uses softmax activation to predict the next token in the sequence.

7. Distributed Training

The model is optimized for distributed training using TensorFlow’s MirroredStrategy, which helps train the model across multiple GPUs, significantly speeding up the process on large datasets.

8. Loss Function and Optimizer

The model uses sparse categorical crossentropy as the loss function, which is ideal for multi-class classification problems.
The Adam optimizer is employed for efficient training and convergence.

Model Usage:

Training: Train the model with pairs of dotless and vowelized (dotted) Arabic text.
Inference: After training, input a dotless Arabic sentence, and the model will output the vowelized version of the text.

Parameters:

vocab_size: Size of the vocabulary (total number of unique tokens in the input and output space).
max_length: Maximum length of input sequences.
latent_dim: Dimension of the embedding and LSTM layers (default is 64).

Example Workflow:

Training: Train the model on a large corpus of paired dotless and vowelized Arabic text.
Inference: Input a dotless Arabic sentence, and the model outputs the vowelized (dotted) version.

Applications:

Automatic Diacritization: Converts dotless Arabic text into vowelized form for better pronunciation and understanding.
Speech Recognition: Useful in improving accuracy in Arabic speech-to-text systems.
Machine Translation: Helps in generating accurate translations with proper vowelization for better meaning preservation.
Educational Tools: Aids in teaching Arabic reading and pronunciation.