This week we feature an article written by Cristina Anselmi, Machine Translation Specialist at EA and returning presenter at Game Global 2020, and Inés Rubio, a localization veteran with 15 years of gaming industry experience.
The original version of this article appeared in the March/April, 2020 edition of Multilingual.
Machine translation (MT) has been a hot topic for a while, especially since the rise of neural machine translation (NMT). The output quality spike is finally shaping solutions to deal with ever-increasing volume and accelerated demand, remarkable even for the prudent gaming industry.
The great spike in MT output quality can be dated back to 2016 when Google launched its neural system (GNMT). This technology has come a long way since the introduction of clumsy rule-based MT or even the improved statistical MT dominating the scene from the 1980s. Google was a pioneer providing easy access to NMT back in 2016, claiming that in some cases, human and GNMT translations were nearly indistinguishable.
MT technology has been available to the video game localization industry on PCs for over 25 years without much success. Any technology that affects the traditional role of translators is not adopted easily, and MT was for a long time regarded as a standalone solution that would replace translators’ roles, instead of a way to assist them with their work. Despite this, with some tweaking and research, MT has proven to work well in many areas: online catalogs, customer-created content and “gisting”, where understanding the general meaning is the main goal. For video games, however, we need to account for gamers’ expectations —
which are rightfully quite high.
Localization is an essential part of the gaming experience for most players around the world. It enables them not only to understand the mechanics of the game and its rules, but allows them to enjoy the gameplay and feel engaged. In other words, quality localization enhances playability beyond mere functionality. That is why the gaming industry is still very cautious when it comes to MT implementation.
Here are 3 challenges that are unique to the in-game text localization:
1 – Terminology
Consistency in terminology is fundamental not only to ensure a good gaming experience but also to prevent noncompliance issues that might hinder the release of the game.
2 – Variables and tags
The presence of variables and tags (Figures 1 and 2) poses a technical challenge. Variables and tags both need to be respected in the translated text, as they will be replaced by the player name (for instance) or by a link to a screen in the game itself. Sometimes they are just cosmetic tags to modify the text format. Mistakes could result in code errors that would provoke functionality and display issues, disrupting playability.
3 – Creativity
Aside from the technical components, one of the biggest challenges with NMT in the gaming industry is creativity. The types of texts can vary a lot from conventional on-screen text, and due to this, the required level of creativity changes. Apart from audio recordings, which by nature need to be quite liberal and natural-sounding, we often come across made-up language or puns and jokes that need to be transferred to the target language.
So how do you choose whether to apply NMT to a specific game, and if so, where do you start?
5 Steps to the implementation of MT in games localization
Despite these challenges, MT can play a big role in the video game localization process. Here is a step by step guide on how to get started.
Step 1: What is the reason behind MT implementation?
The first question that needs to be addressed is why we need to implement MT for video games and what we want to achieve. The answers could be numerous, and most of the time they are related to ever increasing volumes and accelerated translation timelines.
Speed may be one factor. MT can help by being more agile and increasing the speed of delivery, especially in the wake of the continuous delivery approach currently applied throughout the industry.
Figure 1: Sample text with color tags.
Figure 2: Sample texts with variables.
Utility may be another. MT might be needed if only to understand the general meaning of specific documentation for internal purposes, to avoid dedicating precious resources and time to this task.
Cost could be another factor. Machine translation might help you save money.
Each video game company might have a specific reason connected to these criteria, but nevertheless there is one common key factor: fit for purpose. If the quality of the raw output is not good enough, the translations won’t be delivered faster or cheaper, and they will not even be understandable. This is why the first thing to consider with NMT is quality. We will come back to this later.
Step 2: What kind of text?
One important thing to keep in mind is that video game localization does not consist of just the in-game, on-screen text. The biggest chunk of content may come from the in-game
text itself, but MT might be applied to other kinds of content. These can be customer support texts, how-to articles, metadata, marketing text, game packs and so on.
This means that the purpose and readership of the content (and therefore the level of quality you want to achieve) are the primary considerations to keep in mind.
Step 3: How do you measure success?
Success can be measured in different ways, particularly depending on the reasons connected to the implementation of MT. Quality of the output is one of the main parameters to measure success, but there are other things to consider. It is important to measure how much of the text delivered by MT is being edited by post-editors, while also taking into account time-to-market acceleration and productivity increases.
The variety of text types presents different challenges and quality expectations. This means that when assessing quality, it is fundamental to keep in mind the purpose and readership of the content and therefore tailor the relevant quality evaluation method. Readability or accuracy might not be sufficient for game text.
The most commonly used and accepted mathematical MT output quality evaluation method is the BLEU score, a metric that scores translations on a scale of 0 to 1. The closer to 1, the better the translation correlates to a human translation. Put simply, it measures how many words overlap in each translation when compared to a reference translation. Nowadays there are tools on the market that help with tracking these variables, as well as resources like the TAUS DQF BI Report to benchmark the results against other parties.
Step 4: How do you choose an MT provider?
Once the decision to implement MT for games is taken, the next step is the provider selection, which is key to the success of this venture. There are many providers on the market to choose from, depending on whether you prefer to build your own system or if you want an external partner to do it, with the associated costs and time factors.
The quality of the output is an obvious selection criterion. However, in the video game industry, the security of data, especially for unreleased titles, might play an equally important role.
Whether the MT system is built internally or by a service provider, in the case of video games it is rational to use the relevant company’s game translation memories (TMs) to create a model able to produce translations close to the desired style and quality. These TMs will be stored on a cloud during training, and it is imperative that they are not used to either train either another company’s systems, or a general-purpose engine. The training corpus should be stored in a secure environment inaccessible by anyone outside the specific company.
Step 5: How will MT integrate with your workflow and tools?
No matter what partner is selected as the MT provider, the chosen solution will need to be integrated within the current translation workflow. All affected stakeholders should be included, and the translation environment should be prepared to best accommodate this new
technology, as MT should automate the workflow even further and not create additional overhead.
MT is not a standalone solution; it should integrate with the current translation process. This is not a new concept. Augmented translation puts technology and AI in the service of translators. We recommend combining TM output with MT output where the leverage is lower. As an example, why use MT on a segment that already has a 90% match from the TM?
Not only do the technical aspects need to be considered, but you will need to train your teams. They should be able to best take advantage of the new technology with some post-editing courses, practical exercises and a general understanding of this technology. It will not only allow them to perform correctly, but may also help them change their mindset toward MT, which might be the biggest challenge to face.
Did you like this post? Come back next week to find out more about how MT was implemented in the localization process at Electronic Arts.Cristina will also present a case study at our next Game Global conference. Check out the program here!