2025-05-06

Slide 1: Title

SwiftKey Next-Word Predictor
Faster, smarter typing on mobile devices

Slide 2: How It Works

  • Data: Sampled ~40 K lines from en_US blogs, news, Twitter
  • Preprocessing:
    1. Clean & tokenize (lowercase, strip URLs/punctuation)
    2. Build unigram, bigram, trigram counts
  • Model:
    • Back-off trigram: use last 2 words if seen, else bigram on last word
    • Returns top-3 next-word suggestions

Slide 3: Quantitative Performance

  • Coverage: ~95 % of real-world contexts found in our n-grams
  • Top-1 Accuracy: 42 % (exact match)
  • Top-3 Accuracy: 75 % (correct next-word in suggestions)
  • Perplexity: 120 on held-out sample

Slide 4: Live Demo

Slide 5: Next Steps & Ask

1.Personalization: adapt to individual typing habits

2.Multi-Lingual: extend to German, Russian, Finnish

3.Integration: embed into SwiftKey SDK

4.Investment Ask: $200K to productionize and scale to 1M users

Thank you—questions welcome!