Sequence-to-Sequence-Learning-with-Neural-Networks (3).pptx

all main point

1. Sequence to Sequence Learning with Neural Networks Authors: Ilya Sutskever, Oriol Vinyals, Quoc V. Le Institution: Google Research Name:Kampasati KoteswaraRao RollNo:23MCF1R18

2. The Challenge Deep neural networks excel at vision and speech tasks, but traditional architectures struggle with variable-length sequences. This work introduces an end-to-end framework to directly map one sequence to another, solving a fundamental limitation in neural sequence modeling. Variable-Length Problem Inputs and outputs have different, unpredictable lengths End-to-End Learning Direct sequence-to-sequence mapping without intermediate representations

3. Real-World Applications Machine Translation English to French, spanning multiple language pairs Speech Recognition Audio waveforms to text transcription Dialogue Systems Query to response conversation modeling These applications require models that understand context and generate coherent variable-length outputs—a cornerstone capability for modern AI systems.

4. Encoder-Decoder Architecture The model employs a two-stage LSTM-based architecture. The encoder processes the entire input sequence, compressing it into a fixed-length context vector. The decoder then reads this vector and generates the output sequence, one token at a time. Encoder LSTM processes input sequence [A, B, C], producing a dense representation of meaning Decoder LSTM generates output sequence [W, X, Y, Z] conditioned on the context vector

5. Architecture Innovation: Sequence Reversal A critical insight: reversing input sequences significantly improves model performance. When English sentences are reversed before encoding, the decoder learns dependencies more efficiently, reducing the average distance between corresponding words. 1 Forward Input A B C → context vector (default approach) 2 Reversed Input C B A → context vector (improved performance: +4 BLEU) 3 Benefit Shorter dependency paths enable faster gradient flow during backpropagation

6. Mathematical Formulation The model maximizes the conditional probability of the target sequence given the source sequence: Where v represents the fixed-length context vector encoded from the input, and each output token depends on the context and all previously generated tokens. This factorization enables efficient sequential generation while maintaining long-range dependencies.

7. Training Configuration Architecture 4-layer LSTM, 1000 hidden units per layer Dataset WMT'14 English-French: 12M sentences Optimization SGD with learning rate 0.7, gradient clipping Hardware 8 GPUs for parallel training Gradient clipping prevents the exploding gradient problem common in deep RNNs, stabilizing training across long sequences.

8. Key Technical Innovations Input Reversal Reversing source sentences improves BLEU score by 4 points, substantially boosting translation quality through improved gradient flow Beam Search Decoding Instead of greedy word selection, explore multiple hypotheses simultaneously to find higher-probability output sequences Gradient Clipping Normalize gradients exceeding threshold to prevent numerical instability during backpropagation through time Parallelization Distribute computation across 8 GPUs, enabling training on massive datasets in reasonable time

9. Experimental Results The sequence-to-sequence model achieved remarkable results on English-French translation, surpassing traditional statistical machine translation baselines for the first time. The ensemble model achieved BLEU = 34.8, surpassing the statistical baseline and demonstrating that end-to-end neural learning can outperform hand-crafted linguistic features.

10. Key Insights & Impact Long-Range Dependencies LSTMs successfully capture distant relationships between words across entire sentences Semantic Preservation Embedding vectors encode and preserve deep meaning from source sentences Semantic Clustering PCA visualization reveals semantically similar phrases cluster together in embedding space Neural Machine Translation Established the foundation for modern neural translation systems now deployed globally

11. Thank You sir 6