Natural Language Generation (NLG)

The NLG components in DialogueKit are currently template-based. The basic template-based generation works by extracting templates from a set of training data. The templates are the user and agent utterances with the annotations removed. This version can be found here: dialoguekit.nlg.nlg_template.TemplateNLG.

An extended version of the basic template-based implementation which can perform conditional language generation is dialoguekit.nlg.nlg_conditional.ConditionalNLG. Using a conditional may be useful in cases where other attributes than the intent and annotations may be of interest , for example, the user’s satisfaction. This can then be used to select templates that have a different tone based on the conditional.

These two are used in similar ways. The only difference is that the ConditionalNLG can use a conditional in the dialoguekit.core.annotated_utterance.AnnotatedUtterance.metadata as long as the conditional value is a number.

To start using these NLG classes a template needs to be generated. Two methods for doing this are provided:

• dialoguekit.nlg.template_from_training_data.build_template_from_instances()

• dialoguekit.nlg.template_from_training_data.extract_utterance_template()

Usage Example

from dialoguekit.core.annotation import Annotation
from dialoguekit.core.intent import Intent
from dialoguekit.nlg.nlg_template import TemplateNLG
from dialoguekit.nlg.template_from_training_data import (
    extract_utterance_template,
)

template = extract_utterance_template(
    annotated_dialogue_file=ANNOTATED_DIALOGUE_FILE_PATH,
)
nlg = TemplateNLG(response_templates=template)

response = nlg.generate_utterance_text(intent=Intent("COMPLETE"))

ConditionalNLG

The ConditionalNLG class contains two additional parameters to the generate_utterance_text() method.

• conditional: str

• conditional_value: Number

These fields are used to find the utterance that has the closest conditional_value to the utterance metadata field with the key conditional. Note that this necessitates the need of a larger template with multiple values for the conditional.

Custom NLG

Any custom NLG component might be implemented by inheriting from dialoguekit.nlg.nlg_abstract.AbstractNLG.