Choosing a Pipeline

Choosing an NLU pipeline allows you to customize your model and finetune it on your dataset.

The Short Answer

If your training data is in english, a good starting point is using pretrained_embeddings_convert pipeline.

language: "en"
pipeline: "pretrained_embeddings_convert"

In case your training data is multi-lingual and is rich with domain specific vocabulary, use the supervised_embeddings pipeline:

language: "en"
pipeline: "supervised_embeddings"

A Longer Answer

The three most important pipelines are supervised_embeddings, pretrained_embeddings_convert and pretrained_embeddings_spacy. The pretrained_embeddings_spacy pipeline uses pre-trained word vectors from either GloVe or fastText, whereas pretrained_embeddings_convert uses a pretrained sentence encoding model ConveRT to extract vector representations of complete user utterance as a whole. On the other hand, the supervised_embeddings pipeline doesn’t use any pre-trained word vectors or sentence vectors, but instead fits these specifically for your dataset.

Note: These recommendations are highly dependent on your dataset and hence approximate. We suggest experimenting with different pipelines to train the best model.

pretrained_embeddings_spacy

The advantage of pretrained_embeddings_spacy pipeline is that if you have a training example like: “I want to buy apples”, and Rasa is asked to predict the intent for “get pears”, your model already knows that the words “apples” and “pears” are very similar. This is especially useful if you don’t have large enough training data.

pretrained_embeddings_convert

Warning: Since ConveRT model is trained only on an English corpus of conversations, this pipeline should only be used if your training data is in English language.
This pipeline uses ConveRT model to extract vector representation of a sentence and feeds them to EmbeddingIntentClassifier for intent classification. The advantage of using pretrained_embeddings_convert pipeline is that it doesn’t treat each word of the user message independently, but creates a contextual vector representation for the complete sentence.

supervised_embeddings

The advantage of the supervised_embeddings pipeline is that your word vectors will be customised for your domain. For example, in general English, the word “balance” is closely related to “symmetry”, but very different to the word “cash”. In a banking domain, “balance” and “cash” are closely related and you’d like your model to capture that.

Comparing different pipelines for your data

Rasa gives you the tools to compare the performance of both of these pipelines on your data directly.
Note: Intent classification is independent of entity extraction. So sometimes NLU will get the intent right but entities wrong, or the other way around.

Class imbalance

Classification algorithms often do not perform well if there is a large class imbalance. To mitigate this problem, rasa’s supervised_embeddings pipeline uses a balanced batching strategy.
In order to turn it off and use a classic batching strategy include batch_strategy: sequence in your config file.

language: "en"
pipeline:
- name: "CountVectorsFeaturizer"
- name: "EmbeddingIntentClassifier"
  batch_strategy: sequence

Multiple Intents

If you want to split intents into multiple labels, you can only do this with the supervised embeddings pipeline using specific flags in Whitespace Tokenizer.
Here’s an example configuration:

language: "en"
pipeline:
- name: "WhitespaceTokenizer"
  intent_split_symbol: "_"
- name: "CountVectorsFeaturizer"
- name: "EmbeddingIntentClassifier"

Understanding the Rasa NLU Pipeline

In Rasa NLU, incoming messages are processed by a sequence of components. These components are executed one after another in a processing pipeline. If you want to add your own component, for example to run a spell-check or to do sentiment analysis, check out Custom NLU Components.

Each component processes the input and creates an output. The output can be used by any component that comes after this component in the pipeline.

Component Lifecycle

Every component can implement several methods from the Component base class; in a pipeline these different methods will be called in a specific order. Before the first component is created using the create function, a context is created to pass information between the components.

The “entity” object explained

After parsing, the entity is returned as a dictionary. There are two fields that show information about how the pipeline impacted the entities returned.

{
  "text": "show me chinese restaurants",
  "intent": "restaurant_search",
  "entities": [
    { 
      "start": 8,
      "end": 15,
      "value": "chinese",
      "entity": "cuisine",
      "extractor": "CRFEntityExtractor",
      "confidence": 0.854,
      "processors": []
    }
  ]
}

Pre-configured Pipelines

A template is just a shortcut for a full list of components. For example:

language: "en"
pipeline: "pretrained_embeddings_spacy"

Another version of this pipeline includes using MITIE’s featurizer and its multi-class classifier.

Custom pipelines

You don’t have to use a template, you can also run a fully custom pipeline by listing the names of the components you want to use:

pipeline:
- name: "SpacyNLP"
- name: "CRFEntityExtractor"
- name: "EntitySynonymMapper"

Example Configuration

language: "en"
pipeline:
- name: "WhitespaceTokenizer"
- name: "CountVectorsFeaturizer"
- name: "EmbeddingIntentClassifier"