It’s true that machine translation still has its limitations in terms of quality, as compared to post-edited work or the work of human translators. But again, budget is often also a limitation of translation work that entails human intervention, whether MTPE or pure human translation. It’s very interesting how Zendesk was able to overcome the limitations of both sides through the use of web analytics.
When we think of the use cases of web analytics, it’s often in a marketing context, whether the creation of consumer profiles or geotargeting. Zendesk’s strategy is brilliant in that they use the same data to determine how they prioritize their budget allotment for translation. MT works best in conjunction with other types of translation, and this strategy has allowed Zendesk to make use of both MT and MTPE in the best way possible.
With MT and web analytics together, the end result is that dominican republic mobile database Zendesk has been able to save much-needed funds while maintaining quality service where it is needed.
Zendesk uses an MT engine that has been specifically trained for their purposes, which is a good practice. Not all MT engines are built the same, or perform well under a different context.