🧠 Italian to English Translation using Seq2Seq + Attention

This project implements a neural machine translation (NMT) model using an encoder-decoder architecture with attention mechanisms. It translates Italian sentences to English using a dataset of bilingual sentence pairs.

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📌 Project Highlights

• Implements a Seq2Seq model with LSTM-based encoder and decoder
• Explores three attention scoring methods: dot, general, and concatenation
• Uses GloVe embeddings for the English vocabulary
• Evaluates performance using BLEU scores and attention visualizations

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📂 Dataset

• Source: ManyThings.org
• Language Pair: Italian ↔ English
• Format: Tab-separated sentence pairs
• Preprocessing:
  • Sentence length capped at 20 tokens
  • Special tokens <start> and <end> added for decoder training

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🧱 Model Architecture

• Encoder:
  • Embedding layer (50d)
  • LSTM layer with 256 units
• Decoder:
  • Embedding layer (100d)
  • LSTM layer with 256 units
  • Attention mechanism (dot, general, concat scoring)
• Output:
  • Dense layer with softmax activation over vocabulary

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📈 Evaluation & Results

Model Variant	BLEU Score
Encoder-Decoder (no attention)	60.8
Encoder-Decoder + Attention (dot scoring)	65.8
Encoder-Decoder + Attention (general scoring)	66.6
Encoder-Decoder + Attention (concat scoring)	66.8

• Attention mechanisms improved translation quality over baseline
• Concat scoring gave the best BLEU score and alignment maps

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🔍 Sample Output

Input: anche voi riuscite a farlo
Predicted: you can do it too

Input: non riguarda noi
Predicted: it is not about us

(More examples and attention maps can be found in the notebook.)

🛠️ Development Environment

🧪 Virtual Environment

• Key Packages:
  • Python 3.10
  • tensorflow==2.19.0
  • tf-keras==2.19.0
  • keras==3.9.2
  • pandas=1.4.2
  • numpy=1.26.4
  • matplotlib=3.10.0
  • regex=2024.11.6
  • nltk=3.9.1
  • scikit-learn=1.1.1