During their webinar in September 2019, Smartling announced a new feature to their Translation Management System (TMS): Machine Translation (MT) Auto Select. This means that whenever you decide to utilize a specific MT engine in a TMS, the tool will automatically pick the best engine for your particular case.
The idea to spare the linguists the effort on evaluation of the MT output directly in the TMS is not new.
For instance, in Q1 2019, Memsource released Memsource Translate which does a similar trick. Since then, they have been working on the next version that is scheduled for Q2 2020. It will feature more than 30 MT engines that are supported in Memsource, including custom ones (as opposed to only two engines being supported as of now: Google Translate and Microsoft Translator).
Here is an interesting fact shared with us by David Canek, Memsource’s CEO: “When we first launched Memsource Translate, we saw that for 70 percent of projects a sub-optimal engine was selected by the linguist. Another engine would have performed better in terms of translation quality.”
Memsource Translate is powered by a reinforcement learning algorithm that helps recommend the optimal MT engine based on the source and target language. Starting with the new version, the plan is to also add the domain as the third parameter.
The algorithm is constantly evaluating the performance of various MT engines for a given combination of a source language, target language and a domain that is automatically identified from the corpus of the document. It learns continuously based on this evaluation what the best MT engine is.
So, when you translate, say, technical documentation from English to Japanese in Memsource, it will recommend an engine that provides the highest quality for that specific language pair and domain.
Memsource Translate also includes an MTQE feature, which helps evaluate the quality of the MT output: scores are automatically calculated before any post-editing is done. For this one, deep neural network is used.
The language services industry is undergoing a profound transformation with the emergence of cutting-edge technologies such as ChatGPT and large language models (LLMs). These powerful language generation models have captivated the attention of businesses and language professionals alike, offering exciting possibilities for translation, localization, and content creation.
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.