A specific use case worth exploring in this regard is MT for User Generated Content (UGC). Because of the speed with which UGC (comments, feedback, reviews) is being created and the corresponding costs of its professional translation, many organizations turn to MT.
Popular examples of such companies are Skype (in addition to text translation, Microsoft developed the Automatic Speech Recognition (ASR) for audio speech translation in Skype) and Facebook. The social network is aiming to solve the challenge of fine-tuning each system relating to a specific language pair, using neural machine translation (NMT) and benefiting from various contexts for translations.
One solution that tackles this issue is the technology developed by Language I/O. The company’s SaaS platform translates chats, emails, and other UGC instantaneously via MT. It takes into account the client's glossaries and TMs, selects the best MT engine output and then improves on the results using cultural intelligence and/or human linguists who compare machine translations post-facto to ensure that their MT Optimizer engine learns over time.
The solution is powered by Language I/O machine learning technology coupled with an MT aggregator and a proprietary glossary imposition layer. Because the integrated MT engines are constantly learning in their own right, the MT Optimizer needs to continuously improve its ability to select the best MT engine for each translation request based on:
This tech plugs into Salesforce, Oracle, and Zendesk – the CRM platforms where support agents already work. They are also providing their APIs so that anyone with UGC can use the service (not necessarily just customer service personnel).
It’s already been six years now since Google revealed that Google Translate processes 146 billion words a day — three times more than what all the professional translators in the world combined can do in a month. That was 2016 and things haven’t really slowed down in the machine translation (MT) universe since.
We recently introduced you to the two- (or five-) second rule, which is essentially the reaction or decision-making time a linguist should spend judging whether to post-edit a segment of machine translation (MT) output or to retranslate it. This rule of thumb aims to help increase the linguist’s productivity when working with MT.
If you’re a driver, you’ve probably heard of the two-second rule. Staying at least two seconds behind any vehicle is considered a rule of thumb for drivers wanting to maintain a safe following distance at any speed. The two seconds don’t represent safe stopping distance but rather safe reaction time.
Do you remember the last time when people were NOT talking about machine translation (MT)? We don't. Wherever you go, there’s someone talking about MT. With few exceptions, it seems like the only major disruptors in our industry over the past few decades have been breakthroughs in language technology.