There are two main approaches to machine translation development: rule-based programming and statistical learning. In a rule-based programming approach, linguists and software developers create frameworks for how sentences work in different languages. For example, adjectives like colour always come before the noun in English, but come after the noun in Spanish. Finished machine translation programmes work by converting text inputs using these rules.

The statistical approach is slightly more complex. It depends on a lot of translation sample data, which allows the machine translation programme to learn language patterns. Research teams manually gather language-centric information from a number of sources, and computers scan social media and professional publications to get up to date information on language usage. 

Nowadays, most machine translation teams combine both approaches to develop highly sophisticated and capable machine translation platforms.

It’s best to work within your limitations and use a more neutral communication approach when you speak to potential business partners or suppliers.

Use sentences that communicate a single, clear idea and avoid cluttering your text input with polite ramblings or different tenses. For example, “What might you charge for this?” is a more complex (if polite) way of saying “How much do you charge?”.

Navigating these small language differences successfully can help you break through the language barrier and land those all-important deals.