vania-janet/multiturn-rag-retrieval-data huggingface dataset:
Although not an accent mark, the same applies to words using, for example, the Spanish letter ñ vs. the letter n, such as uña vs. una. In this case the letter ñ is not simply an n with an accent; it is a unique, Spanish-specific letter.

You can enable fuzzy matching if you think your customers will not use the appropriate accents, or misspell words (including, for example, putting a n instead of a ñ), or you can explicitly include them in the training examples.

Note: Accent normalization is enabled for Portuguese, Spanish, French, and Czech.

Workspace opt-out flag
: The Watson Assistant REST API now supports an opt-out flag for workspaces. This flag indicates that workspace training data such as intents and entities are not to be used by IBM for general service improvements. For more information, see the [API Reference](https://cloud.ibm.com/apidocs/assistant/assistant-v1?curl=data-collection)





 7 August 2017 

Next and last date interpretation
: The Watson Assistant service treats last and next dates as referring to the most immediate last or next day referenced, which may be in either the same or a previous week. See the [system entities](https://cloud.ibm.com/docs/assistant?topic=assistant-system-entitiessystem-entities-sys-date-time) topic for additional information.





 3 August 2017 

Fuzzy matching for additional languages (Beta)
: Fuzzy matching for entities is now available for additional languages, as noted in the [Supported languages](https://cloud.ibm.com/docs/assistant?topic=assistant-language-support) topic.

Partial match (Beta - English only)
: Fuzzy matching will now automatically suggest substring-based synonyms present in user-defined entities, and assign a lower confidence score as compared to the exact entity match. See [Fuzzy matching](https://cloud.ibm.com/docs/assistant?topic=assistant-entitiesentities-fuzzy-matching) for details.





 28 July 2017 

Updates
: This release includes the following updates:
* For information about the Tune by Example feature, see [Understanding Tune by Example](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-tbe-intro).



* What programming languages can I use?

The service supports SDKs in many popular programming languages and platforms.



* For more information about the SDKs and links to them on GitHub, see [Watson SDKs](https://cloud.ibm.com/docs/text-to-speech?topic=watson-using-sdks).
* For more information about all methods of the SDKs for the Text to Speech service, see the [API & SDK reference](https://cloud.ibm.com/apidocs/text-to-speech).



* What is the maximum amount of text that I can submit for synthesis?

You can submit the following maximum amount of text for a speech synthesis request with each of the service's method:



* HTTP GET /v1/synthesize method - Maximum of 8 KB of total input, which includes the input text, SSML, and the URL and headers.
* HTTP POST /v1/synthesize method - Maximum of 8 KB for the URL and headers. Maximum of 5 KB for the input text, including SSML.
* WebSocket /v1/synthesize method - Maximum of 5 KB of input text, including SSML.



All characters of the input, including whitespace and those that are part of SSML elements, are counted toward the data maximum. For billing purposes, whitespace characters are not counted. For more information, see [Data limits](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-service-featuresfeatures-data-limits).
* How does customization work?

The customization interface of the Text to Speech service creates a dictionary of words and their translations for a specific language. This dictionary is referred to as a custom model.
Managing custom words 

The customization interface includes the POST /v1/customizations/{customization_id}/words and PUT /v1/customizations/{customization_id}/words/{word_name} methods, which are used to add or modify words for a custom model. For more information, see [Add words to the custom language model](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-languageCreateaddWords). The interface also includes the following methods for listing and deleting words for a custom language model.



 Character encoding for custom words 

In general, you are likely to add most custom words from corpora. Make sure that you know the character encoding that is used in the text files for your corpora. The service preserves the encoding that it finds in the text files.

You must use that same encoding when working with individual words in the custom language model. When you specify a word with the GET, PUT, or DELETE /v1/customizations/{customization_id}/words/{word_name} method, you must URL-encode the word_name that you pass in the URL if the word includes non-ASCII characters.

For example, the following table shows what looks like the same letter in two different encodings, ASCII and UTF-8. Although you can pass the ASCII character on a URL as z, you must pass the UTF-8 character as %EF%BD%9A.



Table 1. Examples of character encoding

 Letter Encoding Value 

 z ASCII 0x7a (7a) 
 ｚ UTF-8 hexadecimal 0xEF 0xBD 0x9A (efbd9a) 







 Listing custom words from a custom language model 

The customization interface offers two methods for listing words from a custom language model:



* The GET /v1/customizations/{customization_id}/words method lists information about the words from the custom model's words resource. The method includes two optional query parameters:



* The word_type parameter specifies which words are to be listed:
New Korean and Spanish accessibility in Discovery News : Added the ability to access Korean and Spanish collections to the [IBM Watson™ Discovery News](https://cloud.ibm.com/docs/discovery?topic=discovery-watson-discovery-news) tile in the tooling. Previously, these collections could only be queried via the API.





 23 January 2018 

New query expansion : Added the ability to expand the scope of a query - for example, you can expand a query for "car" to include "automobile" and "motor vehicle". In addition, you can replace commonly misspelled terms, for example, you can replace queries for "seabizcuit" with "seabiscuit." Query expansion is implemented with the Discovery API. See [Query expansion](https://cloud.ibm.com/docs/discovery?topic=discovery-query-conceptsquery-expansion) for details.





 15 January 2018 

New version of Discovery News : IBM Watson™ Discovery News Original was retired from service. It was replaced 31 July 2017 with a new version, named IBM Watson™ Discovery News.





 11 January 2018 

New full supoort for Korean language : Added full support for Korean. For more information, see [Language support](https://cloud.ibm.com/docs/discovery?topic=discovery-language-support).





 15 December 2017 

New Element Classification enrichment : Released the Element Classification enrichment, which parses elements (sentences, lists, tables) in governing documents to classify important categories and types. See [Element classification](https://cloud.ibm.com/docs/discovery?topic=discovery-element-classification) for more information. Element Classification is not available for service instances that are subscribed to the Premium plan. [Resolved](https://cloud.ibm.com/docs/discovery?topic=discovery-release-notes5jun18)

New Simplified Chinese and Dutch language support : Added Basic language support for Simplified Chinese and Dutch. See [Language Support](https://cloud.ibm.com/docs/discovery?topic=discovery-language-support) for more information. Currently, Simplified Chinese and Dutch collections must be created with the API.
* For information about the Tune by Example feature, see [Understanding Tune by Example](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-tbe-intro).







 What programming languages can I use? 

The service supports SDKs in many popular programming languages and platforms.



* For more information about the SDKs and links to them on GitHub, see [Watson SDKs](https://cloud.ibm.com/docs/text-to-speech?topic=watson-using-sdks).
* For more information about all methods of the SDKs for the Text to Speech service, see the [API & SDK reference](https://cloud.ibm.com/apidocs/text-to-speech).







 What is the maximum amount of text that I can submit for synthesis? 

You can submit the following maximum amount of text for a speech synthesis request with each of the service's method:



* HTTP GET /v1/synthesize method - Maximum of 8 KB of total input, which includes the input text, SSML, and the URL and headers.
* HTTP POST /v1/synthesize method - Maximum of 8 KB for the URL and headers. Maximum of 5 KB for the input text, including SSML.
* WebSocket /v1/synthesize method - Maximum of 5 KB of input text, including SSML.



All characters of the input, including whitespace and those that are part of SSML elements, are counted toward the data maximum. For billing purposes, whitespace characters are not counted. For more information, see [Data limits](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-service-featuresfeatures-data-limits).





 How does customization work? 

The customization interface of the Text to Speech service creates a dictionary of words and their translations for a specific language. This dictionary is referred to as a custom model.
curl -X POST --header "Authorization: Bearer {token}" --header "Content-Type: application/json" --header "Accept: audio/wav" --output partial-cheerful-style.wav --data "{"text":"<express-as style='cheerful'>Oh, that's good news!</express-as> Do you need any further help?"}" "{url}/v1/synthesize?voice=en-US_EmmaExpressive"







 Emphasizing interjections 

When you use expressive neural voices, the service automatically detects a number of common interjections based on context. In the resulting audio, it gives them the natural emphasis that a human would use in normal conversation.

Table 2 lists the interjections that the service recognizes and provides examples of how they are pronounced in synthesized speech. The samples use the en-US_AllisonExpressive voice. The table shows the primary spelling of each interjection. The service recognizes alternative spellings for some of the interjections. For example, oh and ohh produce the same sound, as do hmm and hmmm. However, the service produces slightly different pronunciations for other alternative spellings, such as ooh and uhm,



Table 2. Interjections that are emphasized

 Interjection Example sentence Audio sample 

 aha "Aha. So that's the secret." Your browser does not support the audio tag. 
 hmm "Hmm. I'm not sure I understand." Your browser does not support the audio tag. 
 huh "Huh, I hadn't noticed that." Your browser does not support the audio tag. 
 oh "Oh, let me get that for you." Your browser does not support the audio tag. 
 uh "Uh, let me check." Your browser does not support the audio tag. 
 uh-huh "Uh-huh, that's right." Your browser does not support the audio tag. 
 um "That's, um, not quite right." Your browser does not support the audio tag. 





 Enabling or disabling interjections with SSML
* en-US_AllisonExpressive
* en-US_EmmaExpressive
* en-US_LisaExpressive
* en-US_MichaelExpressive



Expressive neural voices offer natural-sounding speech that is exceptionally clear, crisp, and fluid. The new voices are generally available (GA) for production use. They support the use of both standard International Phonetic Alphabet (IPA) and IBM Symbolic Phonetic Representation (SPR) phonetic symbols. For more information, see



* [Supported languages and voices](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-voiceslanguage-voices-expressive)
* [Expressive neural voices](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-voiceslanguage-voices-expressive)



New speaking styles with expressive neural voices
: The expressive neural voices determine the sentiment of the text from the context of its words and phrases. The speech that they produce, in addition to having a very conversational style, reflects the mood of the text. But you can embellish the voices' natural tendencies by indicating that all or some of the text is to emphasize one of the following speaking styles:



* Cheerful - Expresses happiness and good news.
* Empathetic - Expresses empathy or sympathy.
* Neutral - Expresses objectivity and evenness.
* Uncertain - Expresses confusion or uncertainty.



For more information, see [Using speaking styles](https://cloud.ibm.com/docs/text-to-speech?topic=text-to-speech-synthesis-expressivesyntheses-expressive-styles).

New interjection emphasis with expressive neural voices
: With expressive neural voices, the service automatically detects a set of common interjections based on context. When it synthesizes these interjections, it gives them the natural emphasis that a human would use in normal conversation. For some of the interjections, you can use SSML to enable or disable their emphasis.
City, County, Country, Person, Location, Organization, State or province, Town Named Entity Recognition European and similar languages<br><br>Add entries for the language to the appropriate dictionary.<br><br>Arabic, Japanese and similar languages 
 Continent Named Entity Recognition Define a dictionary and specify entries to contain the names of the continents in the target language. If English results are also to be included, form a union between the provided continent extractor with your custom one. 
 Address Named Entity Recognition Define a sequence composed of the parts of the address, such as street, city, country and postal code. 
 All financial extractors Finance actions These extractors cannot be extended. 
 All machine-data analytics extractors Machine Data Analytics When possible, define a regular expression or sequence. The remaining extractors cannot be extended. 
 All sentiment analysis extractors Sentiment Analysis - General Sentiment Analysis - Surveys Add entries in the target language to the Customize Terms tab on the Extractor Properties pane. 









 Deploy advanced rules model to Natural Language Understanding 

Deploying an advanced rules model to Natural Language Understanding is deprecated. As of June 10, 2021, you will not be able to deploy advanced rules models to Natural Language Understanding.



 Export advanced rules project 

These instructions detail how to export an advanced rules model from the IBM Watson® Knowledge Studio for IBM Cloud Pak for Data Advanced Rules Editor, but are common instructions for the public cloud.



1. Open the Advanced Rules workspace
2. Confirm that all models that you want to export are on the Canvas, and are not saved to the "Extractors" pane, except for provided extractors
3. Right click the project that you want to export on the "Projects" pane
4. Click Export Project(images/Spanish.png "Results of applying the Person extractor to Spanish text")export][!!!]
5. Check "Include Documents" if you need to, input a file name and click the OK button
6. Choose Save as File on your browser's popup dialog, then a ZIP file is saved on your computer (usually in a "Download" folder)



When exporting an Advanced Rules Editor project, only concepts that exist on the Canvas are included in the exported ZIP file.
Norwegian (Nyorsk) [Version 2](https://cloud.ibm.com/docs/natural-language-understanding/?topic=natural-language-understanding-entity-types-version-2) [Version 2](https://cloud.ibm.com/docs/natural-language-understanding/?topic=natural-language-understanding-entity-types-version-2) [Version 2](https://cloud.ibm.com/docs/natural-language-understanding/?topic=natural-language-understanding-entity-types-version-2) [Version 2](https://cloud.ibm.com/docs/natural-language-understanding/?topic=natural-language-understanding-entity-types-version-2) [Version 2](https://cloud.ibm.com/docs/natural-language-understanding/?topic=natural-language-understanding-entity-types-version-2) [Version 2](https://cloud.ibm.com/docs/natural-language-understanding/?topic=natural-language-understanding-entity-types-version-2) [Version 2](https://cloud.ibm.com/docs/natural-language-understanding/?topic=natural-language-understanding-entity-types-version-2)
* You can include multiple words that are separated by spaces. The service enforces a maximum of 40 total characters, not including leading or trailing spaces.







 Guidelines for Japanese 



* Use only full-width Katakana characters by using the ― lengthen symbol (chou-on, or 長音, in Japanese). Do not use half-width characters.
* Use contracted sounds (yoh-on, or 拗音, in Japanese) only in the following syllable contexts:

イェ, ウィ, ウェ, ウォ, キィ, キャ, キュ, キョ, ギャ, ギュ, ギョ, クァ, クィ, クェ, クォ

グァ, グォ, シィ, シェ, シャ, シュ, ショ, ジィ, ジェ, ジャ, ジュ, ジョ, スィ, ズィ, チェ

チャ, チュ, チョ, ヂェ, ヂャ, ヂュ, ヂョ, ツァ, ツィ, ツェ, ツォ, ティ, テュ, ディ, デャ

デュ, デョ, トゥ, ドゥ, ニェ, ニャ, ニュ, ニョ, ヒャ, ヒュ, ヒョ, ビャ, ビュ, ビョ, ピィ

ピャ, ピュ, ピョ, ファ, フィ, フェ, フォ, フュ, ミャ, ミュ, ミョ, リィ, リェ, リャ, リュ

リョ, ヴァ, ヴィ, ヴェ, ヴォ, ヴュ
* Use only the following syllables after an assimilated sound (soku-on, or 促音, in Japanese):

バ, ビ, ブ, ベ, ボ, チ, チェ, チャ, チュ, チョ, ダ, デ, ディ, ド, ドゥ, フ

ファ, フィ, フェ, フォ, ガ, ギ, グ, ゲ, ゴ, ハ, ヒ, ヘ, ホ, ジ, ジェ, ジャ

ジュ, ジョ, カ, キ, ク, ケ, コ, キャ, キュ, キョ, パ, ピ, プ, ペ, ポ, ピャ
Do not include any punctuation characters with the word inside of the <say-as> tag.
* The word oh, when used colloquially to indicate the number zero, is never treated as an interjection. For example, the word is not emphasized as an interjection in the following sentence: The number is one oh three.
* The neutral style does not emphasize interjections to the extent of the default expressive voice or the other styles.









 Emphasizing words 

The expressive voices use a conversational style that naturally applies the correct intonation from context. But you can use the SSML <emphasis> element to indicate that one or more words are to be given more or less emphasis in the synthesized audio. The change in stress can be indicated by an increase or decrease in pitch, timing, volume, or other acoustic attributes.

The <emphasis> element supports an optional level attribute that accepts one of the following values:



* none - Prevents the service from emphasizing text that might otherwise be emphasized.
* moderate - Provides a noticeable amount of emphasis to the text. This value is the default if you omit the level attribute.
* strong - Provides a more significant amount of emphasis to the text than the moderate level provides.
* reduced - De-emphasizes the text by tending to reduce its significance in the audio. This level is the opposite of stressing the text.



Table 3 provides examples of each available level for the emphasis element. The samples use the en-US_MichaelExpressive voice. A request that uses the emphasis element fails if a specified level is not one of the supported values.



Table 3. Emphasizing words

 Level of emphasis Example sentence Audio sample 

 No emphasis "I am going to give her the book." Your browser does not support the audio tag. 
 none "I am going to give her the <emphasis level='none'>book</emphasis>." Your browser does not support the audio tag. 
 moderate "I am going to <emphasis level='moderate'>give</emphasis> her the book." Your browser does not support the audio tag. 
 strong "I am going to <emphasis level='strong'>give</emphasis> her the book." Your browser does not support the audio tag.
For more information about the formats that require a content-type specification, see Table 1 in [Audio formats](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-audio-formatsaudio-formats-list). The table's Content-type specification column indicates whether you must specify the content type.

For examples of specifying a content type with each of the service's interfaces, see [Making a speech recognition request](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-basic-request). All of the examples in that topic specify a content type.

When you use the curl command to make a speech recognition request with the HTTP interfaces, you must either specify the audio format with the Content-Type header, specify "Content-Type: application/octet-stream", or specify just "Content-Type:". If you omit the header entirely, curl uses a default value of application/x-www-form-urlencoded.





 Data limits and compression 

The service accepts a maximum of 100 MB of audio data for transcription with a synchronous HTTP or WebSocket request, and 1 GB of audio data with an asynchronous HTTP request. When you recognize long continuous audio streams or large audio files, you need to consider and accommodate these data limits.

One way to maximize the amount of audio data that you can pass with a speech recognition request is to use a format that offers [Compression](https://cloud.ibm.com/docs/speech-to-text?topic=speech-to-text-audio-terminologyaudio-terminology-compression). There are two basic types of compression, lossy and lossless. The audio format and compression algorithm that you choose can have a direct impact on the accuracy of speech recognition.

Audio formats that use lossy compression significantly reduce the size of your audio stream. But compressing the audio too severely can result in lower transcription accuracy. You can't hear the difference, but the service is much more sensitive to such data loss.



 Comparing approximate audio sizes
These modules are customized for different languages with the variance in the customization of this view for a specified input language. Eventually, a user might want a program to use modules where the sought output view is named PersonName, irrespective of the modules that are processed. This expectation is normal, since each module that is customized for a language, domain, or another purpose is expected to produce various results. The consumer of these modules does not need to alter the algorithm of their program to accommodate for varying output view names when the underlying semantics are similar.

In the example, because the alias PersonName is used, the consumer does not need to alter the view name that is sought. However, the results might vary depending on the modules that are processed. In the example, for instance, the resulting matches are French-based (Example 1) and English-based (Example 2).

Example 1: Resulting matches are French-based

The following example defines a view PersonNameFrench and outputs it under an implementation-neutral alias name, 'PersonName'.

module personModuleFrench;

create dictionary FrenchNames as
('Jean', 'Pierre', 'Voltaire', 'Francois', 'Marie', 'Juliette');

create view PersonNameFrench as
extract dictionary 'FrenchNames'
on D.text as name
from Document D;

output view PersonNameFrench as 'PersonName';

Example 2: Resulting matches are English-based

The following example defines a view PersonNameEnglish and outputs it under an implementation-neutral alias name, 'PersonName'.

module personModuleEnglish;

create dictionary EnglishNames as
('John', 'Peter', 'Andrew', 'Francis', 'Mary', 'Juliet');

create view PersonNameEnglish as
extract dictionary 'EnglishNames'
on D.text as name
from Document D;

output view PersonNameEnglish as 'PersonName';

The consumer of the example modules can access the output tuples through the alias name 'PersonName'. The consumer would not need to know the actual module from which the results are fetched.







 The extract statement