Natural Language Understanding (NLU)

The NLU component is responsible for obtaining a structured representation of text utterances. Currently, it entails intent classification, entity recognition, and user satisfaction prediction.

Intent Classification

Thus far two different NLU pipelines are implemented for intent classification.

• Cosine intent classifier dialoguekit.nlu.models.intent_classifier_cosine

• Rasa DIET classifier dialoguekit.nlu.models.diet_classifier_rasa

See below for details on how the Rasa DIET classifier is used in our implementation.

Rasa as a component library

diet_classifier_rasa implement Rasa’s DIET classifier. This is a Dual Intent and Entity Transformer, their paper can be read here.

Normally, one would use Rasa as the underlying platform. Here, instead, we use it as a component in DialogueKit. Rasa is distributed with a Apache 2.0 license, granting us free use.

General idea

In general, the idea is to import the necessary packages and re-implement the Rasa workflow with their components and structures. However, because of how Rasa is structured, DialogueKit objects need to be transformed to Rasa components before use.

Implementation

The implementation is in diet_classifier_rasa; this model can be trained and thus uses multiple Rasa components and structures. Below is a list of all the Rasa imports that are used.

from rasa.engine.graph import ExecutionContext, GraphComponent, GraphSchema
from rasa.shared.nlu.constants import TEXT
from rasa.nlu.featurizers.sparse_featurizer.count_vectors_featurizer import (
    CountVectorsFeaturizer,
)
from rasa.shared.nlu.training_data.message import Message
from rasa.engine.storage.resource import Resource
from rasa.nlu.tokenizers.whitespace_tokenizer import WhitespaceTokenizer
from rasa.engine.storage.local_model_storage import LocalModelStorage
from rasa.shared.nlu.training_data.training_data import TrainingData
from rasa.nlu.classifiers.diet_classifier import DIETClassifier
from rasa.shared.importers.rasa import RasaFileImporter

Entity Extraction

As of now only one implementation exists, the Rasa DIET classifier.

User Satisfaction Prediction

User satisfaction prediction entails the task of predicting a user’s satisfaction with the system, based on the conversation. We model this as a classification task, where, given the previous n user-agent turns, the task of the classifier is to predict the user satisfaction on a scale from 1-5:

1. Very dissatisfied

2. Dissatisfied

3. Normal

4. Satisfied

5. Very satisfied

The current satisfaction classifier is a SVM model pre-trained on english data.