FAQ

Frequently asked questions about ModelFront

What content is it good for?

ModelFront works for many types of content. We built it to support customization from day one.

We're currently serving accurate risk predictions for everything from human translations to high-scale datasets of machine translated user-generated text.

It's not a great fit for very one-off, artistan content like marketing slogans, which require careful handcrafted translation or transcreation of every line.

What is actually returned?

The API returns a risk prediction from 0.0 to 1.0 (0% to 100%) for each line - an original segment and its translation.

See our API docs

How do I get started?

To get started, all you need to send is a few hundred lines of sample data in the source language, and tell us your target languages.

Does it support custom datasets?

Yes! In fact, we ask that clients send at least a bit of sample data - a few hundred rows of source-language text - to guarantee them accuracy and satisfaction. The more data we have, the better!

How much data do we need for a custom model?

Like with machine translation, more is better. 100K segments is a good minimum. It can be from a mix of language pairs. If you're really lacking data we can get some more for you.

How will we know it's working?

We work with clients to define evaluation sets. We work to achieve the target accuracy, and then notify you to try it with another set that we and our models have never seen.

The two key numbers here are: recall - how many of the errors it catches - and precision - how many of the errors it caught are actually errors.

For more information, read about our metrics!

What does a risk prediction score actually mean?

A risk prediction score is just the risk of a critical error.

For example, su in su carro can be translated from Spanish to English as her, his, its, their or your. Without more context, we can't say more about the quality, but we can say that it's risky.

How is risk prediction different than quality estimation?

Traditional quality estimation or post-editing effort estimation correlates mostly with the edit-distance to a reference translation.

That's not always useful, because certain types of errors are much more painful than stylistic variations - especially mistranslated brand, product, person or place names, mistranslated prices, sizes and codes, changes in negation and dropped, inserted or offensive words.

We can shape risk models to your exact needs - whatever bad means for your application or users.

How is risk prediction different than translation memory matching?

Translation memory matching requires a translation memory with significant coverage over the new content. Perfect translation memory matches may still be high risk.

Risk prediction works even independent of any translation memory or reference translations.

Do input quality and length affect risk prediction?

Yes. This is a very interesting topic with no simple answers. We've found that the best way to control for these factors is to work with clients on their custom definition of quality for each type of text.

What is a good risk threshold?

Like most binary classification models, our models implicitly make the cutoff at 0.5 (50%).

We recommend using different threshold for different types of text, based on the value to the user. For example, some products generate much more revenue than others, so the risk threshold should be lower. Zooming in, the title of a product is more important than the description, so the risk threshold should be even lower.

Our API returns the raw risk score precisely so that your application can use it in the best way for you and your users.

Can it leverage translation memories, terminologies or human translations?

Yes. They are not required, but custom models will give you an accuracy boost.

Does it work for any translation engine?

Yes, it can. The models are not specific to any engine. If you have an internal engine or a custom flavor of an engine like AutoML, then for best accuracy we recommend a custom model trained on its output.

Does it work for human translation?

Yes, it can. Clients routinely find errors in human translation with ModelFront. That said, human translations have a very different signature and error typology than machine translations. For example, humans make typos, confuse false friends or simply do not know some very obscure words. Literary human translation or transcreation often involves significant restructuring.

Does it do word-level scores?

No. ModelFront currently returns segment-level scores, which can also be aggregated into paragraph-, document- and corpus-level scores.

Does it give a score for each error type?

Not yet. It is on our roadmap. This will let clients set specific risk thresholds for specific error types, and understand their translation engine's overall error typology on a given content type.

We do offer strict and tolerant models. Tolerant models ignore certain types of cosmetic errors. For example, for user-generated content it can make sense to allow inconsistent casing, punctuation, locale formatting or orthographical variants.

Which languages does it support?

Our technology can handle any of the top 100 languages. Our generic base model currently supports only those for which we have significant high-quality data.

What about direct non-English pairs, like German to French?

Our technology can handle any pairing of the more than 100 languages we support.

However, it's important to know that top machine translation systems, like Google Translate, Microsoft Translator and DeepL, translate via English. This means that the quality is much lower for most pairs.

Read more about direct translation.

Does it support markup tags?

Our current technology can handle markup languages like XML and HTML. It does not check the correctness of the tags, only of the text.

How does it comply with GDPR?

To ensure users' privacy and comply with the EU General Data Protection Regulation, we automatically delete request data.

We also recommend that clients pseudonymize sensitive data before sending it for training and do the same before each prediction.

How fast is it?

Just as fast as translation APIs.

The API supports batch requests. We can also support very large offline jobs, and datacenter co-location.

Can it be integrated from any stack?

Yes. It's a JSON REST API, so it's easy to call from any production-strength programming language.

See our API docs

How much does it cost? What is the business model?

For more information, see our plans and our one-pager!

Have more questions?

Contact us