NLU Service updates

  • Release version: Australia
  • Updated March 12, 2026
  • 2 minutes to read
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    Summary of NLU Service updates

    The NLU Service enhances the system's ability to comprehend natural language, facilitating intelligent actions by predicting intents and entities from user utterances. This service is updated bi-monthly, independent of instance upgrades, ensuring continuous improvement in model training and predictions without requiring user intervention.

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    Key Features

    • Dialog acts were introduced in May 2023 to facilitate natural conversations in the Virtual Agent, supporting affirmations, negations, and modifications in English.
    • All languages have migrated to new language models, resulting in a 10% improvement in intent prediction quality.
    • Customers can now manage irrelevant utterances, enhancing irrelevance detection.
    • End-to-end topic testing in Virtual Agent is simplified by removing the requirement for multiple intents in a model.
    • Updates in March 2023 improved intent/entity detection and reduced latency for system entity detection.
    • January 2023 introduced a new DATE-TIME system entity for English and expanded vocabulary and entity support for multiple languages, significantly boosting prediction quality.

    Key Outcomes

    These updates ensure that customers always use the latest version of the NLU Service, enhancing the overall performance and accuracy of the NLU model. By incorporating user feedback and improving handling of language nuances, the NLU Service continually evolves to meet customer needs, allowing for more effective interactions within the Virtual Agent framework.

    Refer to this documentation so you are up to date with changes to the NLU Service.

    Service update summary

    The NLU Service helps the system to understand natural language and drive intelligent actions. This service trains and predicts intents and entities for a given user utterance in your NLU model so it can understand human-expressed natural language, whether spoken or written. The source of this documentation is KB0953693.

    This service is updated independently of your instance upgrade, and without any action required by you. These updates are done on a bi-monthly basis (once every two months) to improve the quality of NLU model training and predictions. Major updates are aligned with family releases such as Rome, San Diego, Tokyo, etc. Minor updates are automatically updated so you are using the latest version when you retrain an NLU model. While most of these updates don't impact your existing use of NLU, there may be some changes you need to be aware of.

    May 2023 NLU Service update

    • Introduced dialog acts to enable natural mid-conversation in Virtual Agent (VA) and improve conversation fluidity. Affirm, negate, and modify dialog acts are supported in English and enabled by default for all new VA topics.
    • Migrated all languages to use new language models, boosting average intent prediction quality by 10% across all languages.
    • Enabled customers to manage and edit irrelevant utterances for their models to improve irrelevance detection.
    • Removed the requirement for a model to have 2 or more intents in the model, making it easier for end-to-end topic testing in VA.

    March 2023 NLU Service update

    • Improved intent/entity detection through better handling of common words in vocabulary sources.
    • Improved latency and memory utilization for system entity (NER) detection.
    • Updated version support so that customers will need to use newer versions of the NLU Service and cannot point to n-2 releases older than their current glide version.

    January 2023 NLU Service update

    • Created the new DATE-TIME system entity (English only) for use in Virtual Agent.
    • Added vocabulary source and entity support (simple, mapped, and open ended) for the Dutch and Italian languages, and system entity support for Portuguese and Brazilian Portuguese.
    • Upgraded the ServiceNow Language Model for the Danish, Swedish, Finnish, and Norwegian languages, improving their average prediction quality by 17% from Tokyo.
    • Improved the handling of punctuation in entities and special characters in vocabulary sources.
    • Incorporated feedback provided on intent predictions by admin users in the NLU Workbench to improve model training data for Virtual Agent.