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Artificial Intelligence · Major Project
Neural Language Translator
English-to-Hindi translation with seq2seq and transformer models
3-4 weeks build
Overview
Trains English-to-Hindi translation on the public IIT Bombay parallel corpus, twice: first an LSTM encoder-decoder with attention, then a small transformer. Both use subword tokenisation so rare words do not collapse into unknowns. BLEU scores and attention heatmaps in the report show where each model succeeds and where it breaks down.
What makes it stand up
- 01Seq2seq with attention and a transformer built on the same data
- 02SentencePiece subword vocabulary for both languages
- 03Attention heatmaps that show word alignment
- 04BLEU evaluation plus a manual error analysis in the report
- 05Web translator with Devanagari output
Module breakdown
- 01
Corpus
Parallel sentence cleaning, length filtering and splits.
- 02
Tokenisation
Shared SentencePiece models trained on the corpus.
- 03
Seq2seq
Bidirectional LSTM encoder with an attention decoder.
- 04
Transformer
Multi-head attention model trained from scratch.
- 05
Interface
Flask app with beam-search translation and heatmaps.
After this, you will be able to
- Explain attention with your own alignment heatmap
- Defend BLEU as a metric and name its weaknesses
- Demo translating sentences the examiner writes
Technology stack
- Python
- PyTorch
- Transformers
- SentencePiece
- Flask
- Level
- Major Project
- Domain
- Artificial Intelligence
- Build time
- 3-4 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support