Adaptable machine translation: what it is and why it matters

What is adaptable MT and how does it work?

• Customization as a service or self-service. Some MT providers will create custom models for their clients as a service. In this process, a data scientist will typically create a custom NMT model that has been trained with large bilingual data sets curated specifically for a customer or an industry. More recently, some MT platforms allow for businesses to take control of customization themselves, through a variety of self-service tools. Custom models created by the MT provider means that adaptation was performed by experts, while self-service gives you more direct control over the output, assuming you have the bilingual data already at hand (typically in the form of a Translation Memory). This control will be especially relevant if you have content you simply cannot release to a 3rd party.
• Glossaries and feedback. The most thorough way to adapt MT is to engage the AI's deep-learning capabilities through training with large data sets, but not everyone has access to the bilingual data required. Another option is to use smaller data sets such as a glossary or approved revisions to the MT's output by your employees. A glossary (also known as a termlist, terminology or dictionary) is a defined, matched set of terms or phrases that should always be translated a certain way. Company or industry specific acronyms are great examples of glossary entries. These glossaries, or dictionaries, can be applied on top of the NMT output to instantly change content without any additional user intervention. If during the process of translation, users have feedback that they want to see implemented on future translations, some MT providers allow organizations or end users to submit these edits to improve the model. 
• User-initiated or continuous adaptation. As an end user or an organization that has well-structured bilingual data sets, you should consider how you want the models to learn from new data. Will you always want an administrator or a linguist to oversee the model retraining or do you want the models to update themselves with new data sets as they are added? There are benefits to both: continuous adaptation is a great option for organizations that have well-established workflows for capturing good clean bilingual data (e.g., most localization or translation vendors). Once you set the structure of what models should be updated with which Translation Memories, then the NMT model will organically train as you translate more content, creating an automated virtuous quality circle. But for end users who don’t have these workflows set, are just learning about adaptation, or want to continually test out model quality based on varying input parameters, then you would want to have users initiate the adaptation each time.

Why might you want adaptable MT?

• When translating for an external audience (with or without human post-editing). Without any adaptation, the right MT solution could be appropriate for a variety of customer-facing translation use cases. But there will typically be many instances where you'll want to enhance the baseline MT quality to reflect your company's preferred terminology (including what not to translate), as well as your style and tone of voice. By adapting MT to create your own custom models, you can also enhance the productivity of post-editors – human translators who review MT output and amend it if necessary – if you're using them as part of your translation process.
• When you need precise translation of very specific content. What do you do if you need high accuracy in translation and the content in question is too specific for your generic MT models? For example, what if you need a nuanced understanding of legal, scientific or financial content with very specific terminology? Adaptable MT will give you the ability to more accurately translate this content with your existing SMEs and datasets.

Adaptable MT models aren't all created equal

• Adaptation for individual or group. Some self-service adaptable MT solutions can only adapt to single users – which is ideal for individual translators, but probably not optimal for enterprise adaptation. Say, for example, Adam and Annie are colleagues who both have important input on how their business is translating some content. Adaptable MT designed for the individual translator will not include Adam’s feedback into Annie’s future translations. Translation teams or translators may like keeping their edits in a closed environment but most enterprise organizations will want an adaptable MT solution that can facilitate feedback from anyone and – once input is approved – reflect it back to all users. That way, the whole organization benefits from the contributions of everyone, and gets more consistent and relevant MT as a result.
• Domain flexibility. Can you create as many domain-specific models as you want, so that you can (for example) let different sub-brands, business units or departments within your organization reflect their own needs?
• Training data caps. Is there a limit to how many translation units you can use for MT training? If there is, is it large enough to cater to the volume you have available, or will it severely limit your ability to leverage all your available translation assets?
• Ease and speed of use. How user-friendly are the adaptability features? The best self-service solutions empower users with an intuitive user interface for the options they support, whether that be applying overlays, training the model, rolling out each custom iteration (domain and language-pair), or automating the process.