Policies

You can customize the policies your assistant uses by specifying the policies key in your project's config.yml. There are different policies to choose from, and you can include multiple policies in a single configuration. Here's an example of what a list of policies might look like:

Action Selection#

At every turn, each policy defined in your configuration will predict a next action with a certain confidence level. For more information about how each policy makes its decision, read into the policy's description below. The policy that predicts with the highest confidence decides the assistant's next action.

Policy Priority#

In the case that two policies predict with equal confidence (for example, the Memoization and Rule Policies might both predict with confidence 1), the priority of the policies is considered. Rasa Open Source policies have default priorities that are set to ensure the expected outcome in the case of a tie. They look like this, where higher numbers have higher priority:

• 6 - RulePolicy

• 3 - MemoizationPolicy or AugmentedMemoizationPolicy

• 1 - TEDPolicy

In general, it is not recommended to have more than one policy per priority level in your configuration. If you have 2 policies with the same priority and they predict with the same confidence, the resulting action will be chosen randomly.

If you create your own policy, use these priorities as a guide for figuring out the priority of your policy. If your policy is a machine learning policy, it should most likely have priority 1, the same as the TEDPolicy.

Machine Learning Policies#

TED Policy#

The Transformer Embedding Dialogue (TED) Policy is a multi-task architecture for next action prediction and entity recognition. The architecture several transformer encoders which are shared for both tasks. A sequence of entity labels is predicted through a Conditional Random Field (CRF) tagging layer on top of the user sequence transformer encoder output corresponding to the input sequence of tokens. For the next action prediction the dialogue transformer encoder output and system action labels are embedded into a single semantic vector space. We use the dot-product loss to maximize the similarity with the target label and minimize similarities with negative samples.

If you want to learn more about the model, check out our paper and on our youtube channel. where we explain the model architecture in detail.

TED Policy architecture comprises the following steps:

1. Concatenate features for

   • user input (user intent and entities) or user text processed through a user sequence transformer encoder,
   • previous system actions or bot utterances processed through a bot sequence transformer encoder,
   • slots and active forms

   for each time step into an input vector to the embedding layer that precedes the dialogue transformer.

2. Feed the embedding of the input vector into the dialogue transformer encoder.

3. Apply a dense layer to the output of the dialogue transformer to get embeddings of the dialogue for each time step.

4. Apply a dense layer to create embeddings for system actions for each time step.

5. Calculate the similarity between the dialogue embedding and embedded system actions. This step is based on the StarSpace idea.

6. Concatenate the token-level output of the user sequence transformer encoder with the output of the dialogue transformer encoder for each time step.

7. Apply CRF algorithm to predict contextual entities for each user text input.

Configuration:

You can pass configuration parameters to the TEDPolicy using the config.yml file. If you want to fine-tune your model, start by modifying the following parameters:

• epochs: This parameter sets the number of times the algorithm will see the training data (default: 1). One epoch is equals to one forward pass and one backward pass of all the training examples. Sometimes the model needs more epochs to properly learn. Sometimes more epochs don't influence the performance. The lower the number of epochs the faster the model is trained. Here is how the config would look like:

  config.yml

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  policies:

  - name: TEDPolicy

  epochs: 200

• max_history: This parameter controls how much dialogue history the model looks at to decide which action to take next. Default max_history for this policy is None, which means that the complete dialogue history since session restart is taken into account. If you want to limit the model to only see a certain number of previous dialogue turns, you can set max_history to a finite value. Please note that you should pick max_history carefully, so that the model has enough previous dialogue turns to create a correct prediction. See Featurizers for more details. Here is how the config would look like:

  config.yml

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  policies:

  - name: TEDPolicy

  max_history: 8

• number_of_transformer_layers: This parameter sets the number of sequence transformer encoder layers to use for sequential transformer encoders for user, action and action label texts and for dialogue transformer encoder. (defaults: text: 1, action_text: 1, label_action_text: 1, dialogue: 1). The number of sequence transformer encoder layers corresponds to the transformer blocks to use for the model.

• transformer_size: This parameter sets the number of units in the sequence transformer encoder layers to use for sequential transformer encoders for user, action and action label texts and for dialogue transformer encoder. (defaults: text: 128, action_text: 128, label_action_text: 128, dialogue: 128). The vectors coming out of the transformer encoders will have the given transformer_size.

• weight_sparsity: This parameter defines the fraction of kernel weights that are set to 0 for all feed forward layers in the model (default: 0.8). The value should be a number between 0 and 1. If you set weight_sparsity to 0, no kernel weights will be set to 0, the layer acts as a standard feed forward layer. You should not set weight_sparsity to 1 as this would result in all kernel weights being 0, i.e. the model is not able to learn.

The above configuration parameters are the ones you should configure to fit your model to your data. However, additional parameters exist that can be adapted.

Memoization Policy#

The MemoizationPolicy remembers the stories from your training data. It checks if the current conversation matches the stories in your stories.yml file. If so, it will predict the next action from the matching stories of your training data with a confidence of 1.0. If no matching conversation is found, the policy predicts None with confidence 0.0.

When looking for a match in your training data, the policy will take the last max_history number of turns of the conversation into account. One “turn” includes the message sent by the user and any actions the assistant performed before waiting for the next message.

You can configure the number of turns the MemoizationPolicy should use in your configuration:

Augmented Memoization Policy#

The AugmentedMemoizationPolicy remembers examples from training stories for up to max_history turns, just like the MemoizationPolicy. Additionally, it has a forgetting mechanism that will forget a certain amount of steps in the conversation history and try to find a match in your stories with the reduced history. It predicts the next action with confidence 1.0 if a match is found, otherwise it predicts None with confidence 0.0.

Rule-based Policies#

Rule Policy#

The RulePolicy is a policy that handles conversation parts that follow a fixed behavior (e.g. business logic). It makes predictions based on any rules you have in your training data. See the Rules documentation for further information on how to define rules.

The RulePolicy has the following configuration options:

• core_fallback_threshold (default: 0.3): Please see the fallback documentation for further information.

• core_fallback_action_name (default: action_default_fallback): Please see the fallback documentation for further information.

• enable_fallback_prediction (default: true): Please see the fallback documentation for further information.

• check_for_contradictions (default: true): Before training, the RulePolicy will perform a check to make sure that slots and active loops set by actions are defined consistently for all rules. The following snippet contains an example of an incomplete rule:

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  rules:

  - rule: complete rule

  steps:

  - intent: search_venues

  - action: action_search_venues

  - slot_was_set:

  - venues: [{"name": "Big Arena", "reviews": 4.5}]

  - rule: incomplete rule

  steps:

  - intent: search_venues

  - action: action_search_venues

  In the second incomplete rule, action_search_venues should set the venues slot because it is set in complete rule, but this event is missing. There are several possible ways to fix this rule.

  In the case when action_search_venues can't find a venue and the venues slot should not be set, you should explicitly set the value of the slot to null. In the following story RulePolicy will predict utter_venues_not_found only if the slot venues is not set:

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  rules:

  - rule: fixes incomplete rule

  steps:

  - intent: search_venues

  - action: action_search_venues

  - slot_was_set:

  - venues: null

  - action: utter_venues_not_found

  If you want the slot setting to be handled by a different rule or story, you should add wait_for_user_input: false to the end of the rule snippet:

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  rules:

  - rule: incomplete rule

  steps:

  - intent: search_venues

  - action: action_search_venues

  wait_for_user_input: false

  After training, the RulePolicy will check that none of the rules or stories contradict each other. The following snippet is an example of two contradicting rules:

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  rules:

  - rule: Chitchat

  steps:

  - intent: chitchat

  - action: utter_chitchat

  - rule: Greet instead of chitchat

  steps:

  - intent: chitchat

  - action: utter_greet # `utter_greet` contradicts `utter_chitchat` from the rule above

• restrict_rules (default: true): Rules are restricted to one user turn, but there can be multiple bot events, including e.g. a form being filled and its subsequent submission. Changing this parameter to false may result in unexpected behavior.

  Overusing rules

  Overusing rules for purposes outside of the recommended use cases will make it very hard to maintain your assistant as the complexity grows.

Configuring Policies#

Max History#

One important hyperparameter for Rasa Open Source policies is the max_history. This controls how much dialogue history the model looks at to decide which action to take next.

You can set the max_history by passing it to your policy in the policy configuration in your config.yml. The default value is None, which means that the complete dialogue history since session restart is taken in the account.

As an example, let's say you have an out_of_scope intent which describes off-topic user messages. If your bot sees this intent multiple times in a row, you might want to tell the user what you can help them with. So your story might look like this:

For your model to learn this pattern, the max_history has to be at least 4.

If you increase your max_history, your model will become bigger and training will take longer. If you have some information that should affect the dialogue very far into the future, you should store it as a slot. Slot information is always available for every featurizer.

Data Augmentation#

When you train a model, Rasa Open Source will create longer stories by randomly combining the ones in your stories files. Take the stories below as an example:

You actually want to teach your policy to ignore the dialogue history when it isn't relevant and to respond with the same action no matter what happened before.

You can alter this behavior with the --augmentation flag, which allows you to set the augmentation_factor. The augmentation_factor determines how many augmented stories are subsampled during training. The augmented stories are subsampled before training since their number can quickly become very large, and you want to limit it. The number of sampled stories is augmentation_factor x10. By default augmentation is set to 20, resulting in a maximum of 200 augmented stories.

--augmentation 0 disables all augmentation behavior. The memoization based policies are not affected by augmentation (independent of the augmentation_factor) and will automatically ignore all augmented stories.

Featurizers#

In order to apply machine learning algorithms to conversational AI, you need to build up vector representations of conversations.

Each story corresponds to a tracker which consists of the states of the conversation just before each action was taken.

State Featurizers#

Every event in a trackers history creates a new state (e.g. running a bot action, receiving a user message, setting slots). Featurizing a single state of the tracker has two steps:

1. Tracker provides a bag of active features:

   • features indicating intents and entities, if this is the first state in a turn, e.g. it's the first action we will take after parsing the user's message. (e.g. [intent_restaurant_search, entity_cuisine] )

   • features indicating which slots are currently defined, e.g. slot_location if the user previously mentioned the area they're searching for restaurants.

   • features indicating the results of any API calls stored in slots, e.g. slot_matches

   • features indicating what the last bot action or bot utterance was (e.g. prev_action_listen)

   • features indicating if any loop is active and which one

2. Convert all the features into numeric vectors:

   SingleStateFeaturizer uses the Rasa NLU pipeline to convert the intent and bot action names or bot utterances into numeric vectors. See the NLU Model Configuration documentation for the details on how to configure Rasa NLU pipeline.

   Entities, slots and active loops are featurized as one-hot encodings to indicate their presence.

Tracker Featurizers#

It's often useful to include a bit more history than just the current state when predicting an action. The TrackerFeaturizer iterates over tracker states and calls a SingleStateFeaturizer for each state to create numeric input features for a policy. The target labels correspond to bot actions or bot utterances represented as index in a list of all possible actions.

There are two different tracker featurizers:

1. Full Dialogue#

FullDialogueTrackerFeaturizer creates a numerical representation of stories to feed to a recurrent neural network where the whole dialogue is fed to a network and the gradient is backpropagated from all time steps. The smaller dialogues are padded with 0 for all features.

2. Max History#

MaxHistoryTrackerFeaturizer creates an array of previous tracker states for each bot action or bot utterance, with the parameter max_history defining how many states go into each row of input features. If max_history is not specified, the algorithm takes the whole length of a dialogue into account. The smaller dialogues are padded with 0 for all features. Deduplication is performed to filter out duplicated turns (bot actions or bot utterances) in terms of their previous states.

For some algorithms a flat feature vector is needed, so input features should be reshaped to (num_unique_turns, max_history * num_input_features).

Custom Policies#

You can also write custom policies and reference them in your configuration. In the example below, the last two lines show how to use a custom policy class and pass arguments to it.

Deprecated Policies#

Mapping Policy#

Fallback Policy#

Two-Stage Fallback Policy#

Form Policy#