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The efforts made during our first ever Nuance Cares Week were a great source of inspiration for our volunteer and giving efforts in the future. Here are a few of the inspiring highlights from the week:. Led by our first ever community hero award winner Raghu Prasad, enthusiastic employees from the Bangalore North, Hyderabad, Chennai and Coimbatore offices participated in a coordinated volunteer activity called Mothering Dreams, where they created educational aids for Anganwadi children.
Our teams had a great time creating educational materials together while they celebrated in the connectedness of being part of an activity that took place across four offices.
The India teams continue to be leaders in carrying out our Nuance Cares program. Nuance Cares Week inspired the Global Technology Solutions GTS team to sponsor a card decorating event for Cards for Hospitalized Kids , a not-for-profit organization that has sent cards to more than 14, kids in more than hospitals and Ronald McDonald Houses nationwide. Valerie led the group to give back on several other occasions, like when they cleaned the in the summer and when they volunteered at the together in the fall.
This group is a shining example of what it looks like when a team prioritizes spending time together while also aiding the greater good. The team expanded the event into an entire month of giving! We wanted to do a little bit to help. She arranged weekly collections to support the Trussell Trust at the Westminster Foodbank , each week with a different theme. The group donated coats and backpacks during the first week, then moved on to donate food the next week. In their third week, employees donated pudding and sweets to spread cheer.
In the final week, they donated toiletries and hygiene products. Each of these drives were tremendously successful, with the bin overflowing each week.
Together the efforts showed a true dedication to helping others in need feel a little more comfortable and a little more joyful this holiday season. These are just a few of the activities our global teams contributed to their local communities. Our teams in Dublin, Seattle, Melbourne, Mahwah, Burlington, Vienna and Turin hosted food, toy and or coat drives, collecting goods to empower hundreds of people in need. Small teams also volunteered together doing activities like mock interviewing at Career Collaborative , lending an extra and at food pantries, assembling packages of clothes at the Giving Factory and cleaning up local neighborhoods.
This Nuance Cares Week has energized our workforce to go further with their giving and volunteering as part of the Nuance Cares program. AI will enable collaborative care, empower the workforce, and advance health equity. How improving productivity can help build trust in financial services. Learn more about Nuance Communications , Inc, and contact us. Here are a few of the inspiring highlights from the week: Making education aids for children in our India offices Led by our first ever community hero award winner Raghu Prasad, enthusiastic employees from the Bangalore North, Hyderabad, Chennai and Coimbatore offices participated in a coordinated volunteer activity called Mothering Dreams, where they created educational aids for Anganwadi children.
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To read detailed error logs, you can download an errors log file in CSV format. To download the file, click the button. You can download the currently selected loaded data from the Discover tab as a CSV file. This includes, for each sample, any entity annotations identified by the model and displayed in Discover. To download the sample data as CSV, click on the download icon above the table. You can then process the CSV data externally into a format that can be imported into Mix.
For more information about importing data into a model, see Importing and exporting data. If you change the application, associated context tag, environment, or date range using the source selectors, the download option is diabled until you press Reload Samples.
Note that in this case this will clear any filters that were set. Using the insights gained from the Discover tab, you can refine your training data set, build and redeploy your updated model, and finally view the data from your refined model on the Discover tab. Rinse and repeat! You can improve your model and your application over time using an iterative feedback loop. It provides advanced automation tools to help make it more efficient to develop larger or more complex projects and perform more sophisticated work on your NLU models.
For users new to Mix. The Develop tab is more appropriate for smaller DIY projects. The Try panel, as in the Develop tab, allows you to interactively test the model by typing in a new sentence. The Sample Sentences panel gives a unified view of all samples in the project for the currently selected language, of all intent types and all verification statuses. The Optimize tab also gives a unified set of controls to perform operations on samples, whether for a single sample, or a chosen set of samples.
The data is displayed in a table, with one row for each sample and with data displayed for the following columns:. Click on the column header to sort the samples by that column. Click again to sort in the opposite order. As with the Develop tab, when there are a lot of samples, the contents will be divided into pages. Similar to the Develop tab, controls on the bottom of the table let you navigate between pages and change the number of samples per page.
The header bar above the Sample contents column has toggles to control the visibility of:. To filter the samples down to a smaller subset of samples, use the filter panel beside the table. You can filter the samples on these dimensions:. Multiple items to include can be selected in the Intents and Entities filters by clicking the available checkboxes. If there are enough samples fitting the filter criteria, they will be displayed in pages. Clicking Clear all in the filters header resets the selections in the filters to their original defaults and displays all samples.
The Automate data menu appears in the samples actions bar above the samples. Automate data provides options for automating basic tasks of grouping and annotating samples.
Currently this menu supports one automation task, Auto-intent. In future releases, additional automations will be added. Clicking Automate data launches an Automate data popup module. Here, the chosen automation can be selected Currently Auto-intent is the only available automation. Note : Automation can also be applied when importing a file with samples, whether in the Develop tab of Mix. See the Import project data documentation for more details on file import options. Each previously unassigned sample is tentatively labeled with one of a small number of auto-detected intents present within the set of unassigned samples.
If a sample is recognized as fitting the pattern of an already defined intent, Auto-intent suggests this existing intent. When an automation action is initiated, Mix. This involves a check of several things:. These checks assure that you have a robust, up to date model and that the Auto-intent run will give useful results when running automation. When the checks are done, results will be displayed visually in the Automate data pop-up module.
If the checks all pass, you will be able to proceed straightaway with automation using the existing trained model. If any of the checks do not pass, you will be informed and advised of how that impacts the next steps. If there are not enough annotated samples in your training set, you will be advised to add more. You can still continue with the Auto-intent request. If there is no existing trained model or your model is out of date, Mix. This will add some time to the automation process.
This initiates the Auto-intent process. When the run is finished, it returns a suggested intent classification for each previously unassigned sample. When the Auto-intent operation completes, you can view the suggestions. Initially, these suggestions are tentative, and from a verification perspective, they are in the status Intent-suggested.
No intent is yet assigned. If there are any newly identified intents, you should review the new intents to see if any of them need to be merged after the fact. Clicking the checkmark icon accepts a suggestion, while clicking the x icon discards the suggestion. For a sample with a suggestion for an existing intent, accepting the suggestion assigns the sample to that intent and moves the sample from Intent-suggested to Intent-assigned. A toast icon will be displayed to confirm your choice has been applied.
You may find in some cases that Auto-intent will interpret multiple new intents that in reality represent the same intent. The Auto-intent algorithm inclines toward identifying "smaller" intents to give more flexibility to developers. If you find that this has happened, it is relatively simple to merge the two newly identified intents. Then move the samples for example, using bulk move intents from the second new intent to the renamed intent. A Samples editor provides an interface to create and add multiple new samples in one shot.
This serves as a faster way to create new samples. Samples are added as plain text without annotations. Individual samples can have up to a maximum of characters. You can add up to samples at one time using this editor.
If you chose an intent for the samples, the new samples should now appear in Optimize and in Develop under the intent. You can annotate the samples in either of these tabs. If you chose to apply Auto-intent to the samples, the samples will appear in the table of samples with intent suggestions.
You can then proceed to rename any newly detected intents, accept or discard the suggested intents, and annotate the samples. The file upload feature in Optimize is similar to that in Develop , allowing you to upload a text file with samples.
The file upload in Optimize allows for additional functionality however. To add multiple samples at once via a text file upload:. Samples uploaded to a specific intent are attached to that intent. You will want to view suggested intents in Optimize and accept or discard those suggestions.
See Auto intent for more details on Auto-intent. The controls and behavior for individual sample operations are mostly the same as those in the Develop tab. An intent menu available in the Intent column of each sample allows an alternate means to change the intent for a sample.
Sometimes, you may realize that the sample does not fit any of the existing intents. In this case, you can create a new intent directly in the menu. With the intent menu open:. In both cases, the Move Samples menu will open to allow you to move the sample to the new intent and decide how you want to deal with any entities in the sample. As in the Develop tab, you can perform bulk operations on a selected subset of multiple samples at the same time.
The behavior for bulk operations in the Optimize tab is similar to that for bulk operations in the Develop tab, as described in Bulk operations. The key differences are that In the Optimize tab:. As described in the Develop tab bulk operations discussion, making any changes to the samples will deselect any selected samples. This includes all the types of sample changes mentioned under Develop. For the Optimize tab specifically, this also includes:.
Once you have selected the subset of samples, click an icon on the header bar to apply one of the available operations:. The icons for accepting and discarding suggested intents on selected samples will only be active if at least one of the selected samples has a pending auto-intent suggestion. Clicking the bulk accept icon opens a window summarizing the selected samples with samples grouped by suggested intent. For newly identified intents, you need to choose a global rename for the intent.
Only once all newly identified intents have been renamed can you click to accept the suggestions. Sometimes when building an NLU model for your application, you will need to handle user inputs that contain sensitive personally identifiable information PII.
Sensitive PII is personal data, not generally easily accessible from public sources, that alone or in conjunction with other data can identify an individual. When collecting such information during an interaction with a user, it is important to mask this data in logs to protect the users. Once an entity has been marked as sensitive, user input interpreted by the model as relating to the entity at runtime will be masked in call logs. Similarly, entities and contents of variables can be marked as Sensitive in Mix.
In natural language understanding, an ontology is a formal definition of entities, ideas, events, and the relationships between them, for some knowledge area or domain. The existence of an ontology enables mapping natural language utterances to precise intended meanings within that domain. In the context of Mix. An intent identifies an intended action.
For example, an utterance or query spoken by a user expresses an intent to order a drink. As you develop an NLU model, you define intents based on what you want your users to be able to do in your application. You then link intents to functions or methods in your client application logic. Intents are often associated with entities to further specify particulars about the intended action. An entity is a language construct for a property, or particular detail, related to the user's intent.
You can link entities and their values to the parameters of the functions and methods in your client application logic. If an entity applies to a particular intent, it is referred to as a relevant entity for that intent. The idea of relevant entities is important:. Your options for collection method will depend on your chosen data type for the entity. For more details see Data type and collection method compatibility. An entity with list collection method has possible values that can be enumerated in a list.
Other examples of entities using list collection might include song titles, states of a light bulb on or off , names of people, names of cities, and so on. A literal is the range of tokens in a user's utterance or query that corresponds to a certain entity. The literal is the exact literal written or transcribed spoken text. Other literals might be "small", "medium", "large", "big", and "extra large". When you annotate samples, you select a range of text to tag with an entity.
For list-type entities, you can then add the text to the list for the entity. Lists of literals can also be uploaded in. For more information, see Importing entity literals. Literals can be paired with values. In comparison to literals, values are the canonical semantic meaning associated to a literal.
A value specifies the entity and allows the system to act on the user's intent. For example, "small", "medium", and "large" can be paired with values "S", "M", and "L", respectively. Multiple literals can have the same value, which makes it easy to map different ways a user might say an entity into a single common meaning. For example, "large", "big", "very big" could all be given the same value "L". If your project includes multiple languages, you will want to support the various ways that users might ask for an item in their language of choice.
List-based entities created in a project are shared across languages. The values and associated literals connected to the entity, however, are created and managed separately by language. This gives flexibility to handle situations where the value options vary by language and location.
When you add a value-literal pair, this pair will apply to the entity only in the currently selected language. The same value name can be used in multiple languages for the same list-based entity, but the value and its literals need to be added separately in each language.
The new value appears in the list along with the first literal. You can also click there to add new literals that map to the same entity value. Again, the literal-value pairs added will not be automatically added to the other languages in the project. To remove a literal, click the delete icon next to the literal. You are asked to confirm the deletion. This removes the literal from the currently selected language.
It is not always feasible to know all possible literals when you create a model, and you may need the ability to interpret values at runtime. For example, each user will have a different set of contacts on his or her phone.
It is not practical or doable to add every possible set of contact names to your entity when you are building your model in Mix. Dynamic list entities allow you to upload data dynamically in a client application at runtime. Wordsets can either be uploaded and compiled ahead of time or uploaded at runtime. The ASRaaS or NLUaaS runtime can then use this data to provide personalization and to improve spoken language recognition and natural language understanding accuracy.
To define an entity with list collection method as dynamic, check the Dynamic box for this entity. While the values for dynamic data are uploaded in the form of wordsets, it is still important to define a representative subset of literal and value pairs for dynamic list entities. This ensures that the model is trained properly and improves the accuracy of the ASR. Using our contact example, this means that you should include a representative subset of what you expect contact names to look like, and ensure that you have samples with the proper annotation.
When naming your dynamic entities in each model, keep in mind that they are global per application ID across languages and deployed model versions.
An entity with relationship collection method has a specific relationship to one or more existing entities, either an "isA" or a "hasA" relationship. The definition of Y is inherited by X, such as Y's list of literals, as well as any applicable grammars and relationships.
Note that while the definition of the child entity is the same as the parent entity, the child entity picks up differences because of its different role in your samples. For example, say you have a train schedule app and you want to accept queries such as "When is the next train from Boston to New York.
If you annotated the query using STATION for both cases, then you would have no way of determining which is the origin and which is the destination. This would, of course, be time consuming and difficult to manage. Now, you only have one list of stations to manage. You can also make isA relationships to predefined entities. Note that unlike an isA relationship, an entity can have multiple hasA relationships. You would use hasA relationships if the entities in your queries have structure.
However, Nuance recommends that you use hasA relationships only if you have a definite need, since they can be tricky to work with, and the complexity means the NLU models may be less accurate than desired. An example of a definite need is to be able to interpret a query like "put the red block into the green box".
In this case you need a way to associate the color red with the block and the color green with the box. Without using hasA relationships the JSON object returned would be flat and you would not know which color went with which object. Essentially, isA creates a subclass sort of relationship, while hasA creates a relationship of composition.
Note that hasA relationships are not supported in Mix. Note that an entity defined in relationship to custom entities via isA or hasA does not automatically inherit the sensitive flag from the original entities. You need to separately mark the new entity as sensitive.
An entity with regex-based collection method defines a set of values using regular expressions. For example, product or order values are typically alphanumeric sequences with a regular format, such as gro or ABC Similarly, you might use entities with regex-based collection to match account numbers, postal zip codes, confirmation codes, PINs, or driver's license numbers, and other pattern-based formats.
Invalid expressions including empty values are not saved. Note the following points when creating regular expressions for entities with regex-based collection method:. Enclosing in parentheses creates a capture group. In general programming, matching a regex pattern with capture groups on a string returns both the full pattern, and the individual capture groups, in order, packaged as an array.
With Mix. When you use a regex with capture groups, Mix. This is to allow extra flexibility for developers; for example if you want to recognize a date pattern, but only need the month to fulfill the user's intent.
If you need to use a parenthetical group, but want the full pattern match as the value returned for the entity, there are two options:.
Consider this phone number regex-based entity any phone number of format :. Annotating with regex-based entities means identifying the tokens to be captured by the regex-defined value. At runtime the model tries to match user words with the regular expression. An entity with rule-based collection method defines a set of values based on a GrXML grammar file. While regular expressions can be useful for matching short alphanumeric patterns in text-based input, grammars are useful for matching multi-word patterns in spoken user inputs.
A grammar uses rules to systematically describe all the ways users could express values for an entity. Before the new entity is saved or modified , Mix. Creating or modifying a rule-based entity requires your NLU model to be retokenized, which may take some time and impact your existing annotations. Shown here is an example GrXML file. This rule itself consists of a one-of list with two options representing two possible formats for the account number.
Each of these options refers to a sub-rule appearing further on in the file via a ruleref element. At runtime, Mix. If the user utterance matches a pattern, this activates that branch. Tip: The "normalize to probabilities" and "robust compile" parameters are recommended in all rule grammar files.
The first parameter improves recognition accuracy, while the second allows missing pronunciations to be ignored during grammar compilation without this parameter, the compilation fails if a pronunciation cannot be found. The file may not reference any other GrXML files so any dependencies should be included within the file itself. An entity with freeform collection method is used to capture, as a single block, user input that you cannot :. Take the example of an intent for sending a text message to a specified user.
A text message body could be any sequence of words of any length. An important aspect of an entity with freeform collection method is that the meaning of the literal corresponding to the entity is not important or necessary for fulfilling the intent.
In the example of sending a text message, the application does not need to understand the meaning of the message; it just needs to send the literal text as a string to the intended recipient. Consider a sports application, where your samples would include many ways of referring to one sports team, for example, the Montreal Canadiens:.
Since you could enumerate each option, you would make this a list type and annotate it accordingly. Additionally, the NLU engine would learn about the entity from these different ways of referring to the Canadiens. You would not have to enumerate every possible sports team or every possible way to refer to the Canadiens. Consider an SMS messaging application, where samples include the destination phone number. There are billions of possible phone number combinations, so clearly you could not enumerate all the possibilities, nor would it really make sense to try.
However, phone numbers would not be considered freeform input, since there is a fixed, systematic structure to phone numbers that falls under a small set of pattern formats. These patterns can be recognized either with a regex pattern for typed in phone numbers or a grammar for spoken numbers. Another problem with handling a phone number as a freeform entity is that understanding the phone number contents will be necessary to properly direct the message.
When your sample entity includes text that does not have well-defined many-to-one relationships and that cannot be fully enumerated or described with rules or patterns, use the freeform entity type. Consider an SMS app, where it is impossible to list or specify every way that a user may say something to your app. The body of an SMS message could be literally anything. Here is an example of what those annotations might look like:.
Moreover, understanding the contents is not necessary to send the message to its destination. Some important points to remember about recognition of entities using freeform collection method:. Avoid using a freeform entity to collect this type of information—the NLU engine has already been trained on a huge number of values, and you won't benefit from this if you use a freeform entity.
Predefined entities save you the trouble of defining entities that are generally useful in a number of different applications, such as monetary amounts, Boolean values, calendar items dates, times, or both , cardinal and ordinal numbers, and so on.
A predefined entity is not limited to a flat list of values, but instead can contain a complete grammar that defines the various ways that values for that entity can be expressed. A grammar is a compact way of expressing a vast range of possible constructions. It would simply not make sense to try to capture the possible expressions for this entity in a list. For more information, including on specific predefined entities, see Predefined entities. These dialog entities are isA entities that refer to predefined entities.
Dialog entities have shorter, more descriptive names than predefined entities. This can make it easier to develop and maintain your Mix. If your Mix. Dialog entities appear in the Predefined Entities section of the Entities area. Mix adds them when you create your project. Note: The following dialog entities are deprecated and, therefore, may appear in the Custom Entities list. These dialog entities can be edited, renamed, and deleted. You specify tag modifiers by annotating samples.
Your Mix. It can use the NOT modifier to negate the meaning of a single entity. Note how you do not simply annotate the literals "and" and "no" as an entity or tag modifier.
Instead, tag modifiers are the parents of the annotations that they connect or negate. An anaphora often occurs in dialogs and makes it difficult to understand what the user means. For example, consider the following phrases:. This will help your dialog application determine to which entity the anaphora refers, based on the data it has, and internally replace the anaphora with the value to which it refers.
For example, "Drive there" would be interpreted as "Drive to Montreal". Once the entity has been identified as referable, you can annotate a sample containing an anaphora reference to that entity.
The Nuance Mix Platform offers a growing number of languages. To determine the languages locales available to your project, go to the Mix. Dashboard, select your project, and click the Targets tab. For more information, see Build resources. For the complete list of supported languages, see Languages.
Adding notice about relationship collection entities and sensitive data status. For more details, see Handling sensitive information. Minor updates to content in Rule-based. A new Expert organization role opens up permissions to access rule-based entity functionality in Mix. Previously this was only available to Nuance Professional Service users. Minor updates to content in Discover what your users say to clarify behavior of download Discover data functionality in relation to source selectors and filters.
Adding ability to set a data type for entities indicating the type of contents the entity will contain. Data types form a contract between Mix. For more details see Add entities to your model. Updates to Train your model. The format of the CSV log produced when there are issues in training has been updated. The log now also includes warning information as well as error information. The log also contains clearer messages about the sources of any issues.
Updates to Bulk operations under both Develop and Optimize. When the number of samples is large and samples are displayed in pages, you can now select all samples on all pages to apply bulk operations. The Develop tab file upload module has been re-skinned, and a new file upload option has been added to the Optimize tab. The Develop tab file upload gives a simplified interface to upload samples under a single intent via a text file.
The Optimize file upload offers the same, but with additional functionality for power users, allowing for Auto-detection of sample intents, including detection of previously unseen intents.
Updates to Freeform entities to reflect conventions for values for freeform entities. Updates to Change intent to reflect changes to the move sample intents flow. Update to Discover tab enabling export of data as. Update to Verify samples to enable bulk operations changing the verification state of multiple samples at the same time.
Adding new Discover tab. The Mix. For now the data is read-only; additional functionality will be added in future releases, such as ability to export data, assign intents, annotate the data, and add selected samples to your training set.
Update and refactoring of Modify samples and Verify samples sections to reflect updates to the UI of the Develop tab samples view and changes in functionality. Updated Verify samples to reflect the following functionality changes:. Updated List entities. For multilingual Mix projects projects that have more than one language , literal and value pairs are now language specific. This change will also apply to all existing literal and value pairs.
Update in Regex-based to clarify behavior of regex-based entities with capture groups. Update in Annotate your samples to clarify how to annotate entity literals spanning multiple words. Added additional information to Verify samples to explain the impact of the new "intent verified" and "fully verified" states. Note that action is required to approve fully verify entity annotations. This crucial step ensures that models are built with the correct data. You are viewing legacy Mix documentation.
This doc set is no longer actively maintained. Please visit our new site! Go to Mix Docs. Please visit our new site at docs. Creating Mix. About Mix. Note that [Mix. Sample status progress bar A progress bar above the data table gives a visual sense of what proportion of the sample data has been processed through to Annotation-assigned, and is thereby ready to use for training a model.
Personal Data: show or hide personally identifying information PII in the displayed samples. Model available Informs that a trained model is needed and that a new one will be trained before running automations, adding additional latency.
Project data reflected in model Informs that a new model will be trained due to the changes in the data, adding additional latency. Quantity data sent for automation Informs that the automation needs a sufficient volume of samples to be performant. Smaller uploads will have sub-optimal performance.
Auto-annotation Auto-annotation is a feature that works on un-annotated samples Intent-assigned but not Annotation-assigned for a specified intent. Working within the selected intent, Auto-annotation attempts to identify any instances of entities associated with the intent and labels them accordingly.
Run Auto-annotation To run Auto-annotation: 1. Choose an intent from the list of existing intents on which to apply Auto-annotation. This initiates the Auto-annotation process. When the run is finished, it returns a suggested entities annotation for each previously unassigned sample. Accept or discard auto-annotate suggestions When the Auto-annotate operation completes, you can view the suggestions. No annotations are yet assigned. Clicking the checkmark icon will accept the suggestion, while clicking the trash icon will discard the suggestion.
Find and replace Find and Replace fields in the samples actions bar above the table allow you to do a substring search or search and replace on the entire training set. Regex patterns also can be used for the search. Perform find and replace To perform find samples matching a string or pattern and if desired, do a replace : 1.
Samples containing the search substring or matching the regex pattern will be displayed, and if replace was selected, the matches will be replaced with the replacement text.
Annotating with rule-based entities Annotating with rule-based entities means identifying the words to be captured using a rule grammar GrXML file. At runtime, the model tries to match user words with the grammar file. A rule includes some number of items, which represent parts of possible matches for the rule. A rule can look for any one of a set of items matching the rule. Open a new window or Nuance policy. Please call if you need a disability accommodation for any part of the employment process.
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