vania-janet/multiturn-rag-retrieval-data huggingface dataset:
You can change the settings that are applied automatically to disambiguation from the Options page.To edit the disambiguation settings, complete the following steps:<-- <ol> -->1. From the Skills menu, click Options.2. Click Disambiguation.3. In the Disambiguation message field, add text to show before the list of dialog node options. For example, What do you want to do?4. In the Anything else field, add text to display as an additional option that users can pick if none of the other dialog node options reflect what the user wants to do. For example, None of the above.
Keep the message short, so it displays inline with the other options. The message must be less than 512 characters. For information about what your assistant does if a user chooses this option, see Handling none of the above]] ! .
5. If you want to limit the number of disambiguation options that can be displayed to a user, then in the Maximum number of suggestions field, specify a number between 2 and 5.<-- </ol> -->Your changes are automatically saved.You can use the API to modify additional disambiguation settings. These settings include the disambiguation sensitivity, which affects how often disambiguation is triggered and how many choices are included. For more information, see the API Reference]] ! ! ! !!.Next, you must decide which dialog nodes you want to make eligible for disambiguation. From the Skills menu, click Dialog.<-- </section "id="section-dialog-runtime-disambig-edit" "> --><-- <section "id="section-dialog-runtime-disambig-choose-nodes" "> --> Choosing nodes to not show as disambiguation options All nodes are eligible to be included in the disambiguation list. - Nodes at any level of the tree hierarchy are included.
- Nodes that condition on intents, entities, special conditions, context variables, or any combination of these values are included.
For more information about the speech recognition methods, see the [API & SDK reference](https://cloud.ibm.com/apidocs/speech-to-text/speech-to-textservice-endpoint).





 17 March 2022 

Grammar support for next-generation models is now generally available
: Grammar support is now generally available (GA) for next-general models that meet the following conditions:



* The models are generally available.
* The models support language model customization.



For more information, see the following topics:



* For more information about the status of grammar support for next-generation models, see [Customization support for next-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-supportcustom-language-support-ng).
* For more information about grammars, see [Grammars](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-customizationgrammars-intro).



New German next-generation multimedia model
: The service now offers a next-generation multimedia model for German: de-DE_Multimedia. The new model is generally available. It does not support low latency. It does support language model customization (generally available) and grammars (beta).

For more information about all available next-generation models and their customization support, see



* [Next-generation languages and models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-ng)
* [Customization support for next-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-supportcustom-language-support-ng)



Beta next-generation en-WW_Medical_Telephony model now supports low latency
: The beta next-generation en-WW_Medical_Telephony model now supports low latency. For more information about all next-generation models and low latency, see
Overview - Customizing models 

You can extend Natural Language Understanding with custom models for supported feature and language combinations.



  Before you begin 



1.  If you haven't done so already, [get started](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-getting-started) with Natural Language Understanding.
2.  Check [Language support for custom models](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-customizinglanguage-support-for-custom-models) to make sure that the custom model you want to create is supported.
3.  Follow the customization instructions for one of the following features.



*  [Entities and relations](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-entities-and-relations)
*  [Categories (Beta)](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-categories)
*  [Classifications](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-classifications)









  Language support for custom models 

Check the Custom model support columns in the tables on the [Language support](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-language-support) page to see the features that support custom models for each language.





  Specifying training parameters for custom models 

As part of the request to create or update a custom model, you may optionally include a training parameters object that specifies attributes of the model. For details, see feature-specific [Classifications training parameters](https://cloud.ibm.com/docs/natural-language-understanding?topic=natural-language-understanding-classificationsclassification-training-parameters).





  Targeted sentiment for custom model entities 

For English only, you can get sentiment scores for each custom model entity that is detected by the service by setting the sentiment: true option in the entities object. No other languages support targeted sentiment for custom model entities.





  Usage restrictions for custom models 

The maximum number of models that can be trained via the Natural Language Understanding customization API in parallel is 3.
The beta next-generation en-WW_Medical_Telephony understands terms from the medical and pharmacological domains. Use the model in situations where you need to transcribe common medical terminology such as medicine names, product brands, medical procedures, illnesses, types of doctor, or COVID-19-related terminology.

Common use cases include conversations between a patient and a medical provider (for example, a doctor, nurse, or pharmacist):



* My head is hurting. I need an Ibuprofen, please.
* Can you suggest an orthopedist who specializes in osteoarthritis?
* Can you please help me find an internist in Chicago?



The new model is available for all supported English dialects: Australian, Indian, UK, and US. The new model supports language model customization and grammars as beta functionality. It supports most of the same parameters as the en-US_Telephony model, including smart_formatting for US English audio. In addition to those features listed in [Supported features for next-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-ngmodels-ng-features), the model does not support the following parameters: profanity_filter, redaction, and speaker_labels.







 Supported features for next-generation models 

The next-generation models are supported for use with a large subset of the service's speech recognition features. In cases where a supported feature is restricted to certain languages, the same language restrictions usually apply to both previous- and next-generation models.



* For more information about the parameters that you can use with next-generation models, including their language support and whether the parameters are GA or beta, see the [Parameter summary](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-summary).
* For more information about next-generation models' support for customization, see [Customization support for next-generation models](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-custom-supportcustom-language-support-ng).
Using a custom language model for speech recognition 

Once you create and train your custom language model, you can use it in speech recognition requests by using the language_customization_id query parameter. By default, no custom language model is used with a request. You can create multiple custom language models for the same or different domains. But you can specify only one custom language model at a time for a speech recognition request. You must issue the request with credentials for the instance of the service that owns the custom model.

A custom model can be used only with the base model for which it is created. If your custom model is based on a model other than the default, you must also specify that base model with the model query parameter. For more information, see [Using the default model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-models-usemodels-use-default).

For information about telling the service how much weight to give to words from a custom model, see [Using customization weight](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-languageUseweight). For examples that use a grammar with a custom language model, see [Using a grammar for speech recognition](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-grammarUse).



 Examples of using a custom language model 

The following examples show the use of a custom language model with each speech recognition interface. In this case, the custom model that is used is based on the next-generation model en-US_Telephony.



* For the [WebSocket interface](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-websockets), use the /v1/recognize method. The specified custom model is used for all requests that are sent over the connection.

var access_token = {access_token};
var wsURI = '{ws_url}/v1/recognize'
+ '?access_token=' + access_token
These changes are the result of migrating the Watson Assistant platform to Java 17, where locale values are updated by using specifications in [CLDR 39](https://cldr.unicode.org/index/downloads/cldr-39).

To avoid or minimize the impact of similar changes in the future, you can use [Display formats](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-actions-global-settingsactions-global-settings-display-formats).





 18 May 2023 

Differences in contextual entity detection for dialog skills with few annotations
: If you have 10 to 20 examples of contextual entities in your dialog skill, you might see differences in the entities detected due to updates made to address critical vulnerabilities. The impact of these differences is limited to only newly-trained models. Existing models are unaffected. You can mitigate these differences by annotating more examples. For more information, see [Annotation-based method](https://cloud.ibm.com/docs/assistant?topic=assistant-entitiesentities-annotations-overview).





 17 May 2023 

Display iframe inline
: In the web chat, there are now two ways an iframe response can be included:



* As a preview card that describes the embedded content. Customers can click this card to display the frame and interact with the content.
* Inline, meaning within the conversation. This new option is good for smaller pieces of iframe content.



For more information, see [Adding an iframe response](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-respondrespond-add-iframe).





 15 May 2023 

Change to dialog skill context variables named request
: If your dialog skill used a context variable that is named request, it was removed from the response payload of any /message calls in the V1 or V2 API, or through the Watson Assistant user interface. After 15 May 2023, this behavior changes. Watson Assistant doesn't remove context variables that are named request from the response payload anymore.





 5 May 2023
* A larger editing view makes it easier to view all the details of a node as you work on it.
* A node can contain multiple responses, each triggered by a separate condition. For more information see [Multiple responses](https://cloud.ibm.com/docs/assistant?topic=assistant-dialog-overviewdialog-overview-responses).







 5 December 2016 

Updates
: This release includes the following updates:



* New languages are supported, all in Experimental mode: German, Traditional Chinese, Simplified Chinese, and Dutch.
* Two new system entities are available: @sys-date and @sys-time. For details, see [System entities](https://cloud.ibm.com/docs/assistant?topic=assistant-system-entities).







 21 October 2016 

Updates
: This release includes the following updates:



* The Watson Assistant service now provides system entities, which are common entities that can be used across any use case. For details, see [Defining entities](https://cloud.ibm.com/docs/assistant?topic=assistant-entities) and search for Enabling system entities.
* You can now view a history of conversations with users on the Improve page. You can use this to understand your bot's behavior. For details, see [Improving your skill](https://cloud.ibm.com/docs/assistant?topic=assistant-logs).
* You can now import entities from a comma-separated value (CSV) file, which helps with when you have a large number of entities. For details, see [Defining entities](https://cloud.ibm.com/docs/assistant?topic=assistant-entities) and search for Importing entities.







 20 September 2016 

New version 2016-09-20
: To take advantage of the changes in a new version, change the value of the version parameter to the new date. If you're not ready to update to this version, don't change your version date.



* version 2016-09-20: dialog_stack changed from an array of strings to an array of JSON objects.







 29 August 2016 

Updates
: This release includes the following updates:
Repeat this process for each entity mention that you want to annotate.

Be sure to annotate every mention of an entity type that occurs in any user examples that you edit. See [What you don't annotate matters](https://cloud.ibm.com/docs/assistant?topic=assistant-entitiesentities-counter-examples) for more details.
9. Now, click the annotation you just created. A box is displayed that says, Go to: {entity-name}. Clicking that link takes you directly to the entity.

![Verify value computer for product entity](https://cloud.ibm.com/docs-content/v1/content/417db917d18067d9dcc4cf2612564a9a87c3ddc8/assistant/images/oe-go-to-entity.png)

The annotation is added to the entity you associated it with, and the system begins to train itself on the new data.

The term you annotated is added to the entity as a new dictionary value. If you associated the annotated term with an existing entity value, then the term is added as a synonym of that entity value instead of as an independent entity value.

![Shows mobile phones was added as a synonym for device value](https://cloud.ibm.com/docs-content/v1/content/417db917d18067d9dcc4cf2612564a9a87c3ddc8/assistant/images/oe-phones-added.png)
10. To see all of the mentions you annotated for a particular entity, from the entity's configuration page, click the Annotation tab.

![Annotation view selector highlighted](https://cloud.ibm.com/docs-content/v1/content/417db917d18067d9dcc4cf2612564a9a87c3ddc8/assistant/images/oe-annotated.png)

Contextual entities understand values that you have not explicitly defined.
Rich response types support
: Rich response types are now supported in a dialog node with slots. You can display a list of options for a user to choose from as the prompt for a slot, for example. For more information, see [Gathering information with slots](https://cloud.ibm.com/docs/assistant-data?topic=assistant-data-dialog-slots).

Improved Entities, Dialog, and Intents page responsiveness
: The Entities, Dialog, and Intents pages were updated to use a new JavaScript library that increases the page responsiveness. As a result, the look of some graphical user interface elements, such as buttons, changed slightly, but the function did not.

Creating contextual entities is easier
: The process you use to annotate entity mentions from intent user examples was improved. You can now put the intent page into annotation mode to more easily select and label mentions. For more information, see [Adding contextual entities](https://cloud.ibm.com/docs/assistant-data?topic=assistant-data-entitiesentities-create-annotation-based).

Webhook callouts are available
: Add webhooks to dialog nodes to make programmatic calls to an external application as part of the conversational flow. This capability is being introduced as a beta feature. For more details, see [Making a programmatic call from dialog](https://cloud.ibm.com/docs/assistant-data?topic=assistant-data-dialog-webhooks).

Testing improvement
: You can now see the top three intents that were recognized in a test user input from the Try it out pane. For more details, see [Testing your dialog](https://cloud.ibm.com/docs/assistant-data?topic=assistant-data-dialog-tasksdialog-tasks-test).





 3 September 2019 

IBM Watson® Assistant for IBM Cloud Pak® for Data version 1.3 is available
: Watson Assistant for IBM Cloud Pak® for Data version 1.3 is compatible with IBM Cloud Pak® for Data versions 2.1.0.1 and 2.1.0.2.
See [Correcting user input](https://cloud.ibm.com/docs/assistant?topic=assistant-dialog-runtime-spell-check) for more details.

The new system entities are used by new skills
: For new English, Brazilian Portuguese, Czech, Dutch, French, German, Italian, and Spanish dialog skills, the new system entities are enabled automatically. If you decide to turn on a system entity and add it to your dialog, it's the new and improved version of the system entity that is used. For more information, see [New system entities](https://cloud.ibm.com/docs/assistant?topic=assistant-system-entities).





 6 March 2020 

Transfer a web chat conversation to a human agent
: Delight your customers with 360-degree support by integrating your web chat with a third-party service desk solution. When a customer asks to speak to a person, you can connect them to an agent through a service desk solution, such as Zendesk or Salesforce. Service desk support is a beta feature. For more information, see [Adding support for transfers](https://cloud.ibm.com/docs/assistant?topic=assistant-deploy-web-chatdeploy-web-chat-haa).





 2 March 2020 

Known issue accessing logs
: If you cannot access user logs from the Analytics page, ask the owner of the service instance for the skill to change your service level access to make you a Manager of the instance. For more information about access control, see [Managing access to resources](https://cloud.ibm.com/docs/assistant?topic=assistant-access-control).





 1 March 2020 deprecation notice 

March 2020 deprecation notice
: To help us continue to improve and expand the capabilities of the assistants you build with Watson Assistant, we are deprecating some of the older technologies. Support for the older technologies will end in June 2020. Take action now to test and adopt the new technologies, so your skills and assistants will be ready when the old technologies stop being supported.

The following technologies are being deprecated:



* Legacy version of numeric system entities
This documentation is for the classic Watson Assistant experience. To see the documentation for the new Watson Assistant, please go [here](https://cloud.ibm.com/docs/watson-assistant).



 Supported languages 

Watson Assistant supports individual features to varying degrees per language.

Watson Assistant has classifier models that are designed specifically to support conversational skills in the following languages:



Table 1. Supported languages

 Language Language code 

 Arabic ar 
 Chinese (Simplified) zh-cn 
 Chinese (Traditional) zh-tw 
 Czech cs 
 Dutch nl 
 English en-us 
 French fr 
 German de 
 Italian it 
 Japanese ja 
 Korean ko 
 Portuguese (Brazilian) pt-br 
 Spanish es 
 Universal* xx 



* If you want to support conversations in a language for which Watson Assistant doesn't have a dedicated model, such as Russian, use the Universal language model. For more information, see [Adding support for global audiences](https://cloud.ibm.com/docs/assistant?topic=assistant-assistant-language).



 Feature support details 

The following tables illustrate the level of language support available for product features.

In the following tables, the level of language and feature support is indicated by these codes:



* GA: The feature is generally available and supported for this language. Note that features might continue to be updated even after they are generally available.
* Beta: The feature is supported only as a Beta release, and is still undergoing testing before it is made generally available in this language.
* NA: Indicates that a feature is not available in this language.





 Skill support details 



Table 2. Skill support details

 Language [Actions skill](https://cloud.ibm.com/docs/assistant?topic=assistant-skill-actions-add) [Dialog skill](https://cloud.ibm.com/docs/assistant?topic=assistant-skill-dialog-add) [Search skill](https://cloud.ibm.com/docs/assistant?topic=assistant-skill-search-add) 

 English (en) GA GA GA 
 Arabic (ar) GA GA GA
French language beta support added for contextual entities
: You can add contextual entities to French-language dialog skills. For more information about contextual entities, see [Creating entities](https://cloud.ibm.com/docs/assistant?topic=assistant-entitiesentities-annotations-overview).

New API version
: The current API version is now 2020-04-01. The following change was made with this version:



* An integrations property was added to the V2 /message context. The service now expects the context.integrations property to conform to a specific schema in which the allowed values are as follows:



* chat
* facebook
* intercom
* liveengage
* salesforce
* slack
* service_desk
* text_messaging
* voice_telephony
* zendesk





If your app uses a context.integrations property that does not conform to the schema, a 400 error code will be returned.





 31 March 2020 

The web chat integration was updated
: The update adds an isTrackingEnabled parameter. You can add this parameter and set it to false to add the X-Watson-Learning-Opt-Out header to each /message request that originates from the web chat. For more information about the header, see [Data collection](https://cloud.ibm.com/apidocs/assistant/assistant-v2data-collection). For more information about the parameter, see [Configuration](https://integrations.us-south.assistant.watson.cloud.ibm.com/web/developer-documentation/api-configuration).





 26 March 2020 

The Covid-19 content catalog is available in Brazilian Portuguese, French, and Spanish
: The content catalog defines a group of intents that recognize the common types of questions people ask about the novel coronavirus. You can use the catalog to jump-start development of chatbots that can answer questions about the virus and help to minimize the anxiety and misinformation associated with it.