vania-janet/multiturn-rag-retrieval-data huggingface dataset:
The precision of the model means that your assistant can better understand and recognize the goals of even the most colloquial message from a customer.

Use the new universal language model to create an assistant that is fluent even in languages that Watson Assistant doesn't support with built-in models.

To deploy, attach each language skill to its own assistant that you can deploy in a way that optimizes its use by your target audience.

For example, use the web chat integration with your French-speaking assistant to deploy to a French-language page on your website. Deploy your German-speaking assistant to the German page of your website. Maybe you have a support phone number for French customers. You can configure your French-speaking assistant to answer those calls, and configure another phone number that German customers can use.





 Understanding the universal language model 

A skill that uses the universal language model applies a set of shared linguistic characteristics and rules from multiple languages as a starting point. It then learns from the training data that you add to it.

The universal language classifier can adapt to a single language per skill. It cannot be used to support multiple languages within a single skill. However, you can use the universal language model in one skill to support one language, such as Russian, and in another skill to support another language, such as Hindi. The key is to add enough training examples or intent user examples in your target language to teach the model about the unique syntactic and grammatical rules of the language.

Use the universal language model when you want to create a conversation in a language for which no dedicated language model is available, and which is unique enough that an existing model is insufficient.

For more information about feature support in the universal language model, see [Supported languages](https://cloud.ibm.com/docs/assistant?topic=assistant-language-support).





 Creating a skill that uses the universal language model 

To create a skill that uses the universal language model, complete the following steps:



1. From the Skills page, click Create skill.
2. Choose to create either a dialog or actions skill, and then click Next.
: The log webhook now supports messages exchanged with actions skills and search skills, in addition to dialog skills. For more information, see [Logging activity with a webhook](https://cloud.ibm.com/docs/assistant?topic=assistant-webhook-log).





 27 May 2021 

Change to conversation skill choices
: When adding skills to new or existing assistant, the conversation skill choices have been combined, so that you pick from either an actions skill or a dialog skill.

With this change:



* New assistants can use up to two skills, either actions and search or dialog and search. Previously, new assistants could use up to three skills: actions, dialog, and search.
* Existing assistants that already use an actions skill and a dialog skill together can continue to use both.
* The ability to use actions and dialog skills together in a new assistant is planned for 2H 2021.







 20 May 2021 

Actions skill improvement
: Actions now include a new choice, Go to a subaction, for what to do next in a step. This feature lets you can call one action from another action, to switch the conversation flow to another action to perform a certain task. If you have a portion of an action that can be applied across multiple use cases you can build it once and call to it from each action. This new option is available in the And then section of each step. For more information, see [Go to a subaction](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-step-what-nextgo-to-another-action).





 21 April 2021 

Preview button for testing your assistant
: For testing your assistant, the new Preview button replaces the previous Preview tile in Integrations.

New checklist with steps to go live
: Each assistant includes a checklist that you can use to ensure you're ready to go live.

Actions skill improvement
: Actions now include currency and percentage response types.

Learn what's new
: The What's new choice on the help menu opens a list of highlighting recent features.





 14 April 2021 

Actions skill improvement
url: 'https://api.us-south.language-translator.watson.cloud.ibm.com/instances/572b37be-09f4-4704-b693-3bc63869nnnn/v3/identify?version=2018-05-01',
auth: {
'username': 'apikey',
'password': 'nnn'
},
headers: {
"Content-Type":"text/plain"
},
body: [
params.payload.input.text
],
json: true,
};
return rp(options)
.then(res => {
//Set the language property of the incoming message to the language that was identified by Watson Language Translator.
params.payload.context.skills["main skill"].user_defined["language"] = res.languages[0].language;
console.log(JSON.stringify(params))
return params;
})
}
else {
params.payload.context.skills["main skill"].user_defined["language"] = 'none'
return params
}
};
Show more

The second web action in the sequence sends the text to the Watson Language Translator service to translate the input text from the language that was identified in the previous web action into English. The translated string is then sent to your assistant instead of the original text.

The node.js code for the second action in your sequence looks as follows:

let rp = require("request-promise");

function main(params) {
console.log(JSON.stringify(params))
//If the the incoming message is not null and is not English, translate it.
if ((params.payload.context.skills["main skill"].user_defined.language !== 'en') && (params.payload.context.skills["main skill"].user_defined.language !== 'none')) {
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).



 Adding skills to your assistant 

Customize your assistant by adding to it the skills it needs to satisfy your customers' goals.

Conversational skills return responses that are authored by you to answer common questions, while a search skill searches for and returns passages from existing self-service content.

You can add the following types of skills to your assistant:



* Conversational skills: Understand and address questions or requests that your customers typically ask about. You provide information about the subjects or tasks that your users need help with, and how they ask about them, and the product dynamically builds a machine learning model that is tailored to understand the same and similar user requests.



* Actions skill : Offers a simple interface where anyone can build a conversational flow for your assistant to follow. The complex process of training data creation occurs behind the scenes automatically. [Learn more](https://cloud.ibm.com/docs/assistant?topic=assistant-skill-addskill-add-actions-skill)
* Dialog skill: Offers a set of editors that you use to define both your training data and the conversation. The conversation is represented as a dialog tree. You use the graphical dialog editor to create a script of sorts for your assistant to read from when it interacts with your customers. The dialog keys off the common customer goals that you teach it to recognize, and provides useful responses. [Learn more](https://cloud.ibm.com/docs/assistant?topic=assistant-skill-addskill-add-dialog-skill)



If you can't decide which type of conversational skill to create, see [Choosing a conversational skill](https://cloud.ibm.com/docs/assistant?topic=assistant-skills-choose).
* Search skill!
Any edits you then make within the dialog skill for the Development assistant will only affect the Development assistant's dialog skill, even though you’re using data from messages sent to the Production assistant.

Similarly, if you create multiple versions of a skill, you might want to use message data from one version to improve the training data of another version.

You cannot access log data from assistants that were created in other service instances.



 Picking a data source 

The term data source refers to the logs compiled from conversations between customers and the assistant or custom application by which a dialog skill was deployed.

When you open the Analytics page, metrics are shown that were generated by user interactions with the current dialog skill. No metrics are shown if the current skill has not been deployed and used by customers.

To populate the metrics with message data from a dialog skill or skill version that was added to a different assistant or custom application, one that has interacted with customers, complete these steps:



1. Click the Data source field to see a list of assistants with log data that you might want to use.

The list includes assistants that have been deployed and to which you have access. Or you can choose to show a list of other deployments. For more information about other types of deployments, see [Show deployment IDs explained](https://cloud.ibm.com/docs/assistant-data?topic=assistant-data-logslogs-deployment-id-explained).
2. Choose a data source.



Statistical information for the selected data source is displayed.

Notice the list does not include skill versions. To get data that is associated with a specific skill version, you must know the time frame during which a specific skill version was used by a deployed assistant. You can select the assistant as the data source, and then filter the metrics by the appropriate dates to see only log data that was generated by the assistant while it was using the skill version.





 Show deployment IDs explained 

Applications that use the older v1 runtime API must specify a deployment ID in each messages sent using the /message API. This ID identifies the deployed app that the call was made from. The Analytics page can use this deployment ID to retrieve and display logs that are associated with a specific live application.
For more information, see the [API Reference](https://cloud.ibm.com/apidocs/assistant/assistant-v1?curl=listdialognodes).





 14 July 2017 

Slots enhancement
: The slots functionality of dialogs was enhanced. For example, a slot_in_focus property was added that you can use to define a condition that applies to a single slot only. See [Gathering information with slots](https://cloud.ibm.com/docs/assistant?topic=assistant-dialog-slots) for details.





 12 July 2017 

Support for Czech
: Czech language support has been introduced; please see the [Supported languages](https://cloud.ibm.com/docs/assistant?topic=assistant-language-support) topic for additional details.





 11 July 2017 

Test in Slack
: You can use the new Test in Slack tool to quickly deploy your workspace as a Slack bot user for testing purposes. This tool is available only for the IBM Cloud US South region.

Updates to Arabic
: Arabic language support has been enhanced to include absolute scoring per intent, and the ability to mark intents as irrelevant; please see the [Supported languages](https://cloud.ibm.com/docs/assistant?topic=assistant-language-support) topic for additional details. Note that the Watson Assistant service learning models may have been updated as part of this enhancement, and when you retrain your model any changes will be applied.





 23 June 2017 

Updates to Korean
: Korean language support has been enhanced; please see the [Supported languages](https://cloud.ibm.com/docs/assistant?topic=assistant-language-support) topic for additional details. Note that the Watson Assistant service learning models may have been updated as part of this enhancement, and when you retrain your model any changes will be applied.





 22 June 2017 

Introducing slots
: It is now easier to collect multiple pieces of information from a user in a single node by adding slots. Previously, you had to create several dialog nodes to cover all the possible combinations of ways that users might provide the information.
Language support 

IBM Watson™ Knowledge Studio for IBM Cloud Pak for Data provides support for training a model in several languages.

Support for multiple languages does not mean that the product itself is translated. The Knowledge Studio user interfaces, messages, and documentation are available in English only.

You can train a model in the following languages:



*  English (the default language)
*  Arabic
*  Brazilian Portuguese
*  Chinese (Simplified)
*  Chinese (Traditional)
*  Dutch
*  French
*  German
*  Italian
*  Japanese
*  Korean
*  Spanish



Support includes the ability to add documents in these languages to a workspace, add dictionaries, run pre-annotation, use the ground truth editor to annotate documents, and train a machine learning model. When you select a language, the system applies language-specific templates to handle dictionary entries, text tokenization, and sentence segmentation. For information about how the system handles dictionaries in different languages, see [Dictionaries](https://cloud.ibm.com/docs/watson-knowledge-studio-data?topic=watson-knowledge-studio-data-dictionarieswks_dictionaries).

> Note: The rule editor and rule-based model cannot handle bidirectional text, so cannot be used with Arabic documents.

> Restrictions:
>
> Because of character restrictions in the underlying machine learning technology, entries in the type system cannot contain spaces and can include only the following ASCII characters:
>
> 
>
>
>  *  A through Z and a through z
>  *  0 through 9
>  *  The underscore character: _
>
>
> 
>
>
> The following rules also apply:
>
> 
>
>
>  *  The first character in the name must be alphabetical.
>  *  The entity type name length cannot exceed 64 characters.
>  *  The relation type name length cannot exceed 128 characters.
>
>
> 
>
>
> Thus, for example, when a human annotator annotates Japanese text in the ground truth editor, the entity type and relation type names that can be applied will not be in Japanese.



  Related tasks 

[Configuring support for Arabic](https://cloud.ibm.com/docs/watson-knowledge-studio-data?topic=watson-knowledge-studio-data-wks_langsupp_ar)
Over time, you can build dialog responses to answer customer queries that require follow-up questions to clarify the user's meaning or for which a short and clear response is suitable. And you can use search skill responses to address more open-ended customer queries that require a longer explanation. This beta feature is available to users of Premium and Plus service plans only.

See [Building a search skill](https://cloud.ibm.com/docs/assistant?topic=assistant-skill-search-add) for more details.





 14 March 2019 





 4 March 2019 

Simplified navigation
: The sidebar navigation with separate Build, Improve, and Deploy tabs has been removed. Now, you can get to all the tools you need to build a dialog skill from the main skill page.

Improve page is now called Analytics
: The informational metrics that Watson generates from conversations between your users and your assistant moved from the Improve tab of the sidebar to a new tab on the main skill page called Analytics.





 1 March 2019 





 28 February 2019 

New API version
: The current API version is now 2019-02-28. The following changes were made with this version:



* The order in which conditions are evaluated in nodes with slots has changed. Previously, if you had a node with slots that allowed for digressions away, the anything_else root node was triggered before any of the slot level Not found conditions could be evaluated. The order of operations has been changed to address this behavior. Now, when a user digresses away from a node with slots, all the root nodes except the anything_else node are processed. Next, the slot level Not found conditions are evaluated. And, finally, the root level anything_else node is processed. To better understand the full order of operations for a node with slots, see [Slot usage tips](https://cloud.ibm.com/docs/assistant?topic=assistant-dialog-slotsdialog-slots-node-level-handler).
* Strings that begin with a number sign (#) in the context or output objects of a message are no longer treated as intent references.

Previously, these strings were treated as intents automatically.
For more information, see [Adding service desk support](https://cloud.ibm.com/docs/assistant?topic=assistant-deploy-web-chatdeploy-web-chat-haa).

Autolearning has been moved and improved
: Go to the Analytics>Autolearning page to enable the feature and see visualizations that illustrate how autolearning impacts your assistant's performance over time. For more information, see [Empower your skill to learn automatically](https://cloud.ibm.com/docs/assistant?topic=assistant-autolearn).

Search from actions skill
: The actions skill now supports triggering a search that uses your associated search skill from within an action step. For more information, see [Deciding what to do next](https://cloud.ibm.com/docs/assistant?topic=assistant-actionsactions-what-next).

System entities language support change
: The new system entities are now used by all skills except Korean-language dialog skills. If you have a Korean skill that uses the older version of the system entities, update it. The legacy version will stop being supported for Korean skills in March 2021. For more information, see [Legacy system entities](https://cloud.ibm.com/docs/assistant?topic=assistant-legacy-system-entities).

Disambiguation selection enhancement
: When a customer chooses an option from a disambiguation list, the corresponding intent is submitted. With this latest release, a confidence score of 1.0 is assigned to the intent. Previously, the original confidence score of the option was used.

Skill import improvements
: Importing of large skills from JSON data is now processed in the background. When you import a JSON file to create a skill, the new skill tile appears immediately. However, depending on the size of the skill, it might not be available for several minutes while the import is being processed. During this time, the skill cannot be opened for editing or added to an assistant, and the skill tile shows the text Processing.





 23 November 2020 

Deploy your assistant to WhatsApp!
Use the universal language model to create an assistant that is fluent even in languages that Watson Assistant doesn't support with built-in models.

To deploy, use the web chat integration with your French-speaking assistant to deploy to a French-language page on your website. Deploy your German-speaking assistant to the German page of your website. Maybe you have a support phone number for French customers. You can configure your French-speaking assistant to answer those calls, and configure another phone number that German customers can use.

You can enable the download of language data files, in CSV format, so you can translate training examples and assistant responses from English into other languages and use in other assistants. For more information, see [Using multilingual downloads for translation](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-admin-language-supportadmin-language-support-multilingual).





 Understanding the universal language model 

An assistant that uses the universal language model applies a set of shared linguistic characteristics and rules from multiple languages as a starting point. It then learns from the training data that you add to it.

The universal language classifier can adapt to a single language per assistant. It cannot be used to support multiple languages within a single assistant. However, you can use the universal language model in one assistant to support one language, such as Russian, and in another assistant to support another language, such as Hindi. The key is to add enough training examples or intent user examples in your target language to teach the model about the unique syntactic and grammatical rules of the language.

Use the universal language model when you want to create a conversation in a language where no model is available, and which is unique enough that an existing model is insufficient.

For more information about feature support in the universal language model, see [Supported languages](https://cloud.ibm.com/docs/watson-assistant?topic=watson-assistant-admin-language-supportadmin-language-support-codes).





 Integration considerations 

Keep these tips in mind for integrations:
Italian (it) GA Deprecated 
 Japanese (ja) GA Deprecated 
 Korean (ko) GA Deprecated 
 Portuguese (Brazilian) (pt-br) GA Deprecated 
 Spanish (es) GA Deprecated 
 Universal (xx) GA NA 



The Watson Assistant service supports multiple languages as noted, but the tool interface itself (descriptions, labels, etc.) is in English. All supported languages can be input and trained through the English interface.

GB18030 compliance: GB18030 is a Chinese standard that specifies an extended code page for use in the Chinese market. This code page standard is important for the software industry because the China National Information Technology Standardization Technical Committee has mandated that any software application that is released for the Chinese market after September 1, 2001, be enabled for GB18030. The Watson Assistant service supports this encoding, and is certified GB18030-compliant







 Changing a skill language 

Once a skill has been created, its language cannot be modified. If it is necessary to change the supported language of a skill, you can do so by editing the skill's underlying JSON.

To change the skill language, take the following steps:



1. Download the skill that you want to edit.
2. Open the downloaded skill JSON file in a text editor.
3. Search for the property named language.

The language property is set to the original language of the skill. For example, the language property is en for an English skill.
4. Change the value of this property to the language you want to use instead. For example, change it to fr for French or de for German.
5. Save the changes to the JSON file, and then upload the edited file, overwriting the existing skill.







 Configuring bidirectional languages 

For bidirectional languages, such as Arabic, you can change your skill preferences.



1. From your skill tile, click the Actions drop-down menu, and then select Language Preferences.

This option is only available for skills that are configured to use a bidirectional language.
2. Select from the following options for your skill: