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.

Connect with Spence:
📍 LinkedIn:   / spencegreen  
📍 Personal Website: http://www.spencegreen.com/

---

⏳ Timestamps:
0:00 Sneak peek, 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

🌟 Transcript: http://wandb.me/gd-spence-green 🌟

Links:
1. Models and Inference for Prefix-Constrained Machine Translation (Wuebker et al., 2016)
- https://aclanthology.org/P16-1007/
- Phrase-based and neural translation approaches to completing partial translations
2. Sequence to Sequence Learning with Neural Networks (Sutskever et al., 2014)
- https://arxiv.org/abs/1409.3215
- The first neural MT paper
3. Neural Machine Translation by Jointly Learning to Align and Translate (Bahdanau et al., 2014)
- https://arxiv.org/abs/1409.0473
- One of the earliest neural MT papers
4. GNOME Project
5. Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation (Johnson et al., 2016)
- Training multi-source, multi-target NMT models
6. Achieving Human Parity on Automatic Chinese to English News Translation (Awadalla et al., 2018)
- "Human parity has been achieved"
7. Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation (Freitag et al., 2021)
- "Human parity has not been achieved"
8. Models and Inference for Prefix-Constrained Machine Translation (Wuebker et al., 2016)
- The problems that Lilt faced in going from prototype to production

---

🎙 Get our podcasts on these platforms:
Soundcloud: http://wandb.me/soundcloud
Apple Podcasts: http://wandb.me/apple-podcasts
Spotify: http://wandb.me/spotify
Google: http://wandb.me/gd_google
YouTube: http://wandb.me/youtube


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