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#24: What Makes Machine Translation Work? - Olga Beregovaya

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In this episode of The Deep View Conversations, we explore the world of machine translation and artificial intelligence with Olga Beregovaya — Smartling’s VP of Machine Translation and Artificial Intelligence. Olga takes us through the evolution of machine translation, from rule-based systems to statistical models to today’s neural networks and large language models.

She sheds light on how translation has shifted from being a purely linguistic endeavor to one that now sits at the intersection of data science, AI, and human creativity. We dive into hallucinations, under-resourced languages, the rise of synthetic data, and what it truly means to maintain quality in a multilingual world. If you've ever wondered what makes AI translation tick — and where it's going — this is the episode for you.

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Artificial intelligence is a complicated topic, bound by a number of complex threads — technical science, ethics, safety, regulation, neuroscience, psychology, philosophy, investment and — above all — humanity. On The Deep View: Conversations we break it all down, cutting through the hype to make clear what's important and why you should care.

Technology

Science

English

United States

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IN THIS EPISODE

Olga Beregovaya

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SHOW NOTES 🔗

00:00:00– Intro

00:01:20– Meet Olga Beregovaya: Linguist to AI executive

00:03:00– Rule-based machine translation: Where it all began

00:05:15– From rules to statistics: The SMT era

00:07:30– Neural networks and the Transformer revolution

00:10:20– Where linguists fit in today’s AI loop

00:12:45– Ad Break

00:14:00– The limits of automation in high-risk content

00:16:30– Has the translator job been reinvented?

00:19:00– The future of post-editing and model training

00:21:30– Can we trust AI without hallucinations?

00:24:00– Building translation systems at Smartling

00:27:00– Terminology, glossary enforcement, and sentence fluency

00:30:00– Translation memory and AI-enhanced fuzzy matching

00:31:30– Smartling’s model strategy

00:33:30– What quality training data looks like

00:35:00– Supporting under-resourced languages

00:38:00– Quality estimation, assurance, and model evaluation

00:40:00– Dealing with linguistic complexity

00:42:30– Rules meet AI: A hybrid approach

00:45:00– Staying at the cutting edge

00:47:00– Final thoughts: Translators, technologists, and tomorrow

00:49:00– Why translation is still a human story at heart

TRANSCRIPT 🔗

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