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Spence shares his experience creating a product around human-in-the-loop machine translation, and explains how machine translation has evolved over the years.
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Spence Green is co-founder and CEO of Lilt, an AI-powered language translation platform. Lilt combines human translators and machine translation in order to produce high-quality translations more efficiently.
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🌟 Show notes:
- http://wandb.me/gd-spence-green
- Transcription of the episode
- Links to papers, projects, and people
⏳ Timestamps:
0:00 Sneak peak, intro
0:45 The story behind Lilt
3:08 Statistical MT vs neural MT
6:30 Domain adaptation and personalized models
8:00 The emergence of neural MT and development of Lilt
13:09 What success looks like for Lilt
18:20 Models that self-correct for gender bias
19:39 How Lilt runs its models in production
26:33 How far can MT go?
29:55 Why Lilt cares about human-computer interaction
35:04 Bilingual grammatical error correction
37:18 Human parity in MT
39:41 The unexpected challenges of prototype to production
---
Get our podcast on these platforms:
👉 Apple Podcasts: http://wandb.me/apple-podcasts
👉 Spotify: http://wandb.me/spotify
👉 Google Podcasts: http://wandb.me/google-podcasts
👉 YouTube: http://wandb.me/youtube
👉 Soundcloud: http://wandb.me/soundcloud
Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning:
http://wandb.me/slack
Check out Fully Connected, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more:
https://wandb.ai/fully-connected
By Lukas Biewald4.8
6868 ratings
Spence shares his experience creating a product around human-in-the-loop machine translation, and explains how machine translation has evolved over the years.
---
Spence Green is co-founder and CEO of Lilt, an AI-powered language translation platform. Lilt combines human translators and machine translation in order to produce high-quality translations more efficiently.
---
🌟 Show notes:
- http://wandb.me/gd-spence-green
- Transcription of the episode
- Links to papers, projects, and people
⏳ Timestamps:
0:00 Sneak peak, intro
0:45 The story behind Lilt
3:08 Statistical MT vs neural MT
6:30 Domain adaptation and personalized models
8:00 The emergence of neural MT and development of Lilt
13:09 What success looks like for Lilt
18:20 Models that self-correct for gender bias
19:39 How Lilt runs its models in production
26:33 How far can MT go?
29:55 Why Lilt cares about human-computer interaction
35:04 Bilingual grammatical error correction
37:18 Human parity in MT
39:41 The unexpected challenges of prototype to production
---
Get our podcast on these platforms:
👉 Apple Podcasts: http://wandb.me/apple-podcasts
👉 Spotify: http://wandb.me/spotify
👉 Google Podcasts: http://wandb.me/google-podcasts
👉 YouTube: http://wandb.me/youtube
👉 Soundcloud: http://wandb.me/soundcloud
Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning:
http://wandb.me/slack
Check out Fully Connected, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more:
https://wandb.ai/fully-connected

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