This presentation provides a comprehensive overview of the encoder-decoder architecture used in modern machine translation systems. It covers the foundational concepts of sequence-to-sequence modeling, tokenization techniques, training data preparation, and decoding strategies. The slides are structured to guide learners through the evolution of MT systems, from basic BPE tokenization to advanced decoding methods like beam search and Minimum Bayes Risk (MBR).
Title: Encoder-Decoder Architecture in Machine Translation Contents: Introduction to Encoder-Decoder Models
Sequence-to-sequence modeling
Transformer-based architecture
Sentence-level translation focus
Training the System
Supervised learning with parallel corpora
Tokenization using subword units
Shared vocabulary for source and target languages
Tokenization Algorithms
Byte Pair Encoding (BPE)
WordPiece algorithm
Unigram/SentencePiece tokenization
Comparative analysis of tokenization methods
Training Data Preparation
Parallel corpora examples (e.g., UN, EU Parliament)
Sentence alignment using dynamic programming
Corpus cleanup strategies
Encoder Architecture
Input embedding and positional encoding
Self-attention and layer normalization
Feed-forward networks and residual connections
Decoding Strategies
Greedy decoding and its limitations
Beam search: hypothesis generation and pruning
Minimum Bayes Risk decoding: error minimization and evaluation metrics
Usage: This presentation is ideal for:
Students and researchers in NLP and machine translation
Educators teaching deep learning-based MT systems
Developers building or analyzing translation models
Format: PowerPoint (.pptx)
Slide-based structure with clear section breaks
Includes algorithmic explanations and practical examples