NLU Training Data

The goal of NLU (Natural Language Understanding) is to extract structured information from user messages. This usually includes the user's intent and any entities their message contains. You can add extra information such as regular expressions and lookup tables to your training data to help the model identify intents and entities correctly.

Training Examples#

NLU training data consists of example user utterances categorized by intent. To make it easier to use your intents, give them names that relate to what the user wants to accomplish with that intent, keep them in lowercase, and avoid spaces and special characters.

Entities#

Entities are structured pieces of information inside a user message. For entity extraction to work, you need to either specify training data to train an ML model or you need to define regular expressions to extract entities using the RegexEntityExtractor based on a character pattern.

When deciding which entities you need to extract, think about what information your assistant needs for its user goals. The user might provide additional pieces of information that you don't need for any user goal; you don't need to extract these as entities.

See the training data format for details on how to annotate entities in your training data.

Synonyms#

Synonyms map extracted entities to a value other than the literal text extracted in a case-insensitive manner. You can use synonyms when there are multiple ways users refer to the same thing. Think of the end goal of extracting an entity, and figure out from there which values should be considered equivalent.

Let's say you had an entity account that you use to look up the user's balance. One of the possible account types is "credit". Your users also refer to their "credit" account as "credit account" and "credit card account".

In this case, you could define "credit card account" and "credit account" as synonyms to "credit":

Then, if either of these phrases is extracted as an entity, it will be mapped to the value credit. Any alternate casing of these phrases (e.g. CREDIT, credit ACCOUNT) will also be mapped to the synonym.

See the training data format for details on how to include synonyms in your training data.

Regular Expressions#

You can use regular expressions to improve intent classification and entity extraction in combination with the RegexFeaturizer and RegexEntityExtractor components in the pipeline.

Regular Expressions for Intent Classification#

You can use regular expressions to improve intent classification by including the RegexFeaturizer component in your pipeline. When using the RegexFeaturizer, a regex does not act as a rule for classifying an intent. It only provides a feature that the intent classifier will use to learn patterns for intent classification. Currently, all intent classifiers make use of available regex features.

The name of a regex in this case is a human readable description. It can help you remember what a regex is used for, and it is the title of the corresponding pattern feature. It does not have to match any intent or entity name. A regex for a "help" request might look like this:

The intent being matched could be greet,help_me, assistance or anything else.

Try to create your regular expressions in a way that they match as few words as possible. E.g. using \bhelp\b instead of help.*, as the later one might match the whole message whereas the first one only matches a single word.

Regular Expressions for Entity Extraction#

If your entity has a deterministic structure, you can use regular expressions in one of two ways:

Regular Expressions as Features#

You can use regular expressions to create features for the RegexFeaturizer component in your NLU pipeline.

When using a regular expression with the RegexFeaturizer, the name of the regular expression does not matter. When using the RegexFeaturizer, a regular expression provides a feature that helps the model learn an association between intents/entities and inputs that fit the regular expression.

Regex features for entity extraction are currently only supported by the CRFEntityExtractor and DIETClassifier components. Other entity extractors, like MitieEntityExtractor or SpacyEntityExtractor, won't use the generated features and their presence will not improve entity recognition for these extractors.

Regular Expressions for Rule-based Entity Extraction#

You can use regular expressions for rule-based entity extraction using the RegexEntityExtractor component in your NLU pipeline.

When using the RegexEntityExtractor, the name of the regular expression should match the name of the entity you want to extract. For example, you could extract account numbers of 10-12 digits by including this regular expression and at least two annotated examples in your training data:

Whenever a user message contains a sequence of 10-12 digits, it will be extracted as an account_number entity. RegexEntityExtractor doesn't require training examples to learn to extract the entity, but you do need at least two annotated examples of the entity so that the NLU model can register it as an entity at training time.

Lookup Tables#

Lookup tables are lists of words used to generate case-insensitive regular expression patterns. They can be used in the same ways as regular expressions are used, in combination with the RegexFeaturizer and RegexEntityExtractor components in the pipeline.

You can use lookup tables to help extract entities which have a known set of possible values. Keep your lookup tables as specific as possible. For example, to extract country names, you could add a lookup table of all countries in the world:

When using lookup tables with RegexFeaturizer, provide enough examples for the intent or entity you want to match so that the model can learn to use the generated regular expression as a feature. When using lookup tables with RegexEntityExtractor, provide at least two annotated examples of the entity so that the NLU model can register it as an entity at training time.

Entities Roles and Groups#

Annotating words as custom entities allows you to define certain concepts in your training data. For example, you can identify cities by annotating them:

However, sometimes you want to add more details to your entities.

For example, to build an assistant that should book a flight, the assistant needs to know which of the two cities in the example above is the departure city and which is the destination city. Berlin and San Francisco are both cities, but they play different roles in the message. To distinguish between the different roles, you can assign a role label in addition to the entity label.

You can also group different entities by specifying a group label next to the entity label. The group label can, for example, be used to define different orders. In the following example, the group label specifies which toppings go with which pizza and what size each pizza should be.

See the Training Data Format for details on how to define entities with roles and groups in your training data.

The entity object returned by the extractor will include the detected role/group label.

In order to properly train your model with entities that have roles and groups, make sure to include enough training examples for every combination of entity and role or group label. To enable the model to generalize, make sure to have some variation in your training examples. For example, you should include examples like fly TO y FROM x, not only fly FROM x TO y.

To fill slots from entities with a specific role/group, you need to define a from_entity slot mapping for the slot and specify the role/group that is required. For example:

Entity Roles and Groups influencing dialogue predictions#

If you want to influence the dialogue predictions by roles or groups, you need to modify your stories to contain the desired role or group label. You also need to list the corresponding roles and groups of an entity in your domain file.

Let's assume you want to output a different sentence depending on what the user's location is. E.g. if the user just arrived from London, you might want to ask how the trip to London was. But if the user is on the way to Madrid, you might want to wish the user a good stay. You can achieve this with the following two stories:

BILOU Entity Tagging#

The DIETClassifier and CRFEntityExtractor have the option BILOU_flag, which refers to a tagging schema that can be used by the machine learning model when processing entities. BILOU is short for Beginning, Inside, Last, Outside, and Unit-length.

For example, the training example

is first split into a list of tokens. Then the machine learning model applies the tagging schema as shown below depending on the value of the option BILOU_flag:

The BILOU tagging schema is richer compared to the normal tagging schema. It may help to improve the performance of the machine learning model when predicting entities.