Localization options for Virtual Agent
The ServiceNow platform provides several methods for serving your international Virtual Agent users, regardless of their language and locale. Professionally localize your conversations, use dynamic machine translation, or a combination of the two.
Direct and dynamic language support
If your chat audience is diverse, you want to support them in their own language whenever possible. One option is to localize everything professionally using linguists who can translate your topics into the desired languages. While this option produces the best user outcomes, it is often the most expensive. Another option available to you is language detection combined with dynamic machine translation. Instead of using translated topics and models, dynamic translation connects to a third-party machine translator. During the conversation, user utterances and responses are translated into the primary language on the instance, and all results are translated back into the user's language for the session. There are benefits and drawbacks for all options, as described in the following list. Before activating machine translations, you should carefully weigh the costs and benefits and set proper expectations for your deployment objectives.
- Direct language support (professional localization)
- A direct language strategy employs an expert linguist to configure entities, intents,
strings, keywords, and vocabulary, which are then used to match user utterances with
configured content. Since an expert provides translations that directly match the
conversation's content, the accuracy of the translated content will be high. In addition to
the translations themselves, experts can use the
NLU vocabulary feature
to further define words and phrases that
it may encounter from your users. For example, a user may enter an acronym such as
MS, which you can define as the vocabulary item
Microsoft.
Professional translations are implemented directly in Virtual Agent Designer. For details, see Localizing Virtual Agent conversations.
Tableau 1. Using translations from professional linguists and corresponding ServiceNow language plugins Benefits Constraints - More accurate translations
- For supported languages, NLU intents and entities are localized
- Requires more resources to build and maintain
- Longer completion time
- Higher implementation costs
- 5 languages have NLU intent and entity support
- 12 languages have NLU intent support only
- Increased coverage is not typically scalable
- Not easily extensible
For more information, see Language support for NLU services.
- Dynamic language detection and machine translation
- Dynamic machine translation relies on the translation service to translate all content in
real time. The accuracy of the translations is dependent on the translation service. Although
translation services typically leverage algorithms to translate words based on the context of
the conversation or sentence, there may be times when there is minimal context for the
algorithm to leverage. Accuracy may be diminished in this situation. Check with your
translation service provider about the ability to exclude words from translation or associate
vocabulary with specific synonyms.
Tableau 2. Using dynamic translation with the supported ServiceNow language plugins Benefits Constraints - Lower implementation costs
- Faster completion time
- Less human maintenance
- ServiceNow provides 22 language plugins with basic translations
- Limited scalability
- Limited extensibility
- 5 languages have NLU intent and entity support
- 12 languages have NLU intent support only
For more information, see Language support for NLU services.
Dynamic machine translation for languages that are not supported on the ServiceNow platform provides coverage, but the quality of the user experience may be lower.Tableau 3. Using dynamic translation with unsupported languages Benefits Constraints - Lower implementation costs
- Faster completion time
- Less human maintenance
- Support for many languages
- Highly scalable
- Highly extensible
- User experience may vary
- Potential for mistranslation may reduce user outcomes
Multi-language deployment strategies
Depending on the needs of your user base, you may want a mix of solutions to maximize both user experience and access. For example, you may choose to professionally localize prioritized languages and dynamically translate others. For example, if 95% of your users speak six languages, you may decide to use a linguist to translate your topics into those six. You might then use Dynamic Translation for the remaining five percent.
You can also enable locale support by designating translated languages as fallback languages. For example, you can support Mexican Spanish users by designating Spanish as the fallback language for that locale.
In the following example, French, German, and Spanish are professionally localized, including the NLU models, intents, and entities. This localized content provides the best user experience. Polish is supported as a language on the ServiceNow platform, and when the plugin is installed and activated, it includes some translated content. In this imaginary scenario, the business decision was made to dynamically translate Polish content because it accounts for a smaller percentage of users. The quality of this experience may vary since NLU models are not localized. Instead, utterances are dynamically translated to English, and NLU topic discovery is run through the English model. Since Czech is currently not a supported language on the ServiceNow platform, dynamic machine translation is the only option. Admins can add Czech as a language and enable Dynamic Translation for the language in Virtual Agent.
Localization insights
You can view dynamic translation and localization insights from the Virtual Agent Designer page. Navigate to , and select Localization Insights from the list of links in the side panel.
For more information, see Localization Insights dashboard.