The gaming industry accounts for 26% of total revenue in the media industry and it is projected to increase to USD 196 billion by 2022. The purpose of this report is to provide a comprehensive picture of the total addressable market (TAM) for multilingual user-generated content (UGC) for player support in the gaming industry.
Using high-precision and high-performance QA tools not only helps improve the quality of a text but can also speed up the turnaround time of localization projects, and can in turn lead to cost savings.
In this report, we will discuss the tools and processes that have helped fuel a multilingual revolution in remote recording for the entertainment industry.
In December 2020, Nimdzi was given an opportunity to test a brand new product — Spotlight. It is developed by Intento to support machine translation (MT) curation, enabling quick analysis of the MT training results. This product is intended mainly for those who train custom MT models and thus regularly face the task of evaluating MT quality.
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.
Instructional videos are a big deal. When was the last time that you went to YouTube to watch a video on how that smartphone or that car or that fancy vacuum cleaner worked before deciding on buying it? Or comparing different brands to see which one spoke to you the most? We bet it wasn’t that long ago!
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.
Some machine translation providers are holding out hope for MT systems that adapt to document context. Could this development eliminate the need for custom MT engines? Will context-enabled MT help MT achieve human parity? Will we still need to customize a few years from now? Let’s discuss further.