Policies

Your project's config.yml file takes a policies key which you can use to customize the policies your assistant uses. 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 assistant's next action is then decided by the policy that predicts with the highest confidence.

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 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:

5. RulePolicy

6. MemoizationPolicy and AugmentedMemoizationPolicy

7. TEDPolicy

In general, it is not recommended to have more than one policy per priority level. 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 Rasa machine learning policies.

Machine Learning Policies#

TED Policy#

The Transformer Embedding Dialogue (TED) Policy is described in our paper.

This policy has a pre-defined architecture, which comprises the following steps:

• concatenate user input (user intent and entities), previous system actions, slots and active forms for each time step into an input vector to pre-transformer embedding layer;

• feed it to transformer;

• apply a dense layer to the output of the transformer to get embeddings of a dialogue for each time step;

• apply a dense layer to create embeddings for system actions for each time step;

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

It is recommended to use state_featurizer=LabelTokenizerSingleStateFeaturizer(...) (see Featurization of Conversations for details).

Configuration:

Configuration parameters can be passed as parameters to the TEDPolicy within the configuration file. If you want to adapt 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.

• hidden_layers_sizes: This parameter allows you to define the number of feed forward layers and their output dimensions for dialogues and intents (default: dialogue: [], label: []). Every entry in the list corresponds to a feed forward layer. For example, if you set dialogue: [256, 128], we will add two feed forward layers in front of the transformer. The vectors of the input tokens (coming from the dialogue) will be passed on to those layers. The first layer will have an output dimension of 256 and the second layer will have an output dimension of 128. If an empty list is used (default behavior), no feed forward layer will be added. Make sure to use only positive integer values. Usually, numbers of power of two are used. Also, it is usual practice to have decreasing values in the list: next value is smaller or equal to the value before.

• number_of_transformer_layers: This parameter sets the number of transformer layers to use (default: 1). The number of transformer layers corresponds to the transformer blocks to use for the model.

• transformer_size: This parameter sets the number of units in the transformer (default: 128). The vectors coming out of the transformers 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 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 a story in the training data. If so, it will predict the next action from the matching story 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. 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): After training, the RulePolicy will perform a check to make sure that there are no rules that contradict with each other, or with any stories. The following snippet is an example for two contradicting rules:

  Copy

  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 Change this parameter to false at your own risk. 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 Core 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 yaml file. 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 Rasa Core 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, by default Rasa Core will create longer stories by randomly gluing together the ones in your stories files. This is because if you have stories like:

You actually want to teach your policy to ignore the dialogue history when it isn't relevant and just 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 we 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, we 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 a couple 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. Please see NLU Model Configuration 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 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#