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Today, we’ve got a Q&A style interview with HubSpot’s Dierk Runne, a senior manager who leads the product localization team and oversees their AI efforts.
I wanted to find out if LLMs are making localization easier or if legacy systems (like neural machine translation) are still more reliable.
On y va!
- Where AI fits into HubSpot's localization efforts
- Machine translation versus LLMs
- Error detection with LLMs
- Integrating LLMs into your workflow + challenges
- Recommendations for small to medium-sized businesses
- Most overrated claim about LLMs
- The impact of LLMs on localization
What do you do at HubSpot?
The localization team at HubSpot is basically an internal service provider within the company for any team, stakeholder, or department with a translation/localization need.
Not sure if we want to get into the difference between the two here?
Tell us, what’s the difference?
My go-to example is this: Translating a British car to an American road would mean simply moving it. Localizing it would mean putting the steering wheel on the other side because you drive on the other side of the road in the US.
Localization means more adaptation to the local market and more editorial freedom.
Got it! Now back to your role and team.
We work with marketing a lot as we have a fairly content-heavy marketing playbook at HubSpot. Another major use case is our knowledge base that’s available in multiple languages.
We have roughly 30 people on the team, including trained linguists and translators, but only for our five "core" languages (other than English), that help us operate in all the languages in which we have a website.
Beyond that, we work with translation agencies and freelancers for our outsourcing needs, and our team takes care of the coordination, the budget management, and the communications management for these external resources.
I’m the manager for product localization and look after our software localization. I‘m also the tech guy on the localization team, meaning when it comes to questions around how we use AI, you’ve come to the right person!
That’s always good to hear. Where does AI fit into the work you do, if at all?
In the current conversation, when people refer to AI, they mostly mean generative AI. With translation, the use of AI goes back a lot further.
In fact, some of the systems that the current LLMs are built on, like the transformer technology, was initially developed for machine translation purposes by Google. Google Translate, for example, has been running on a transformer model called BERT since roughly 2016.
It's the same underlying terminologies, what changed with generative AI is mostly the scale.
Our team has been using machine translation for a long time. This is typically referred to as neural machine translations (NMT), meaning it's based on neural network architecture and typically transformer architecture.
We've been using that at scale since 2019 and the biggest use case that we have is HubSpot's knowledge base (KB). The knowledge base has over a thousand articles that get updated extremely frequently because, obviously, our product updates all the time and the articles have to be kept aligned with the changes in the software.
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