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One of the biggest challenges in localization is making the translation sound as natural as possible. And that means that machine translations will potentially always lack the creative element that sometimes makes the whole game shine. A translator might have a sudden flash of inspiration and come up with an unexpected phrasing or wording that resonates with players much better than the original text.Ĭan a machine be creative? Not yet. It may sound like a given, but creativity plays a massive role in bringing games to life, especially when it comes to their translation. So what's so challenging about translating them properly? The main reason for this is that gaming (regardless of the type of game) always aims for an immersive experience, and one core part of that experience is natural-sounding dialogue and in-game text.
While machine translation works fine in many industries, game localization turned out to be a tough nut to crack. The many intricacies of game localization But their biggest drawback is that they don't consider the complexity of game localization. Commercial stock NMTs offer paid subscriptions with their APIs and integration options.
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The chief advantage of these solutions is that most of them are public and free to use (like Google Translate). This means that when using a stock NMT tool you get a general understanding of the text's meaning, but you don't get an accurate translation of specific phrases and words.Įxamples of stock NMT engines include Google Cloud Translation, Amazon Translate, DeepL Translator, CrossLang, Microsoft Translator, Intento, KantanMT. While these datasets are vast and rich (for example, Google's database), they are not domain-oriented. Stock NMT engines are based on general translation data. We will talk about custom (or niche-specific) NMT tools a bit later for now, let's focus on stock NMT. These can be divided into two main categories: stock and custom NMT engines. It's no surprise that there are dozens of ready-made NMT solutions on the market.
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This makes NMT much more accurate than PEMT and, with sufficient learning, delivers high-quality results much faster than any manual translation. NMT uses neural networks and deep learning to not just translate the text but actually learn the terminology and its specifics. This proved a much more reliable and robust solution. So naturally this method results in numerous mistakes, creating a need for manual editing.Īs time passed and technology advanced, NMT (neural machine translation) came into play. The main problem with PEMT is that the machine translates without comparing the text to previous or current translations and a glossary - it just translates as it "sees" it. PEMT stands for post-edited machine translation: it means that after a machine translates your text, translators go through it and edit it. When machine learning was introduced to localization, it was seen as a great asset, and for quite a while localization companies worked using the PEMT approach.
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In this article we aim to find out what's hot (and what's not) about machine translation (MT) and how to get the most out of it without sacrificing quality.
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Software providers claim that their machine translation products mark a new era in localization, but gamers are often left wishing that game publishers would pay more attention to detail.Īs a professional localization company that currently is working with machine translation post-editing, Alconost could not pass up the topic. Machine learning has made its way into nearly every industry, and game localization is no exception.