3.5. Macro

How to use macro functions for matching in Natex.

The most powerful aspect of Natex is its ability to integrate pattern matching with arbitrary code. This allows you to integrate regular expressions, NLP models, or custom algorithms into Natex.

Creation

A macro can be defined by creating a class inheriting the abstract class Macro in STDM and overrides the run method:

from emora_stdm import Macro, Ngrams
from typing import Dict, Any, List

class MacroGetName(Macro):
    def run(self, ngrams: Ngrams, vars: Dict[str, Any], args: List[Any]):
        return True

#1: imports Macro from STDM.
#2: imports type hints from the typing package in Python.
#4: creates the MacroGetName class inheriting Macro.
#5: overrides the run method declared in Macro.

Currently, the run method returns True no matter what the input is.

Integration

Let us create transitions using this macro. A macro is represented by an alias preceded by the pound sign (#):

transitions = {
    'state': 'start',
    '`Hello. What should I call you?`': {
        '#GET_NAME': {
            '`It\'s nice to meet you.`': 'end'
        },
        'error': {
            '`Sorry, I didn\'t understand you.`': 'end'
        }
    }
}

macros = {
    'GET_NAME': MacroGetName()
}

#4: calls the macro #GET_NAME that is an alias of MacroGetName.
#13: creates a dictionary defining aliases for macros.
#14: creates an object of MacroGetName and saves it to the alias GET_NAME.

To call the macro, we need to add the alias dictionary macros to the dialogue flow:

df = DialogueFlow('start', end_state='end')
df.load_transitions(transitions)
df.add_macros(macros)

#3: adds all macros defined in macros to the dialogue flow df.

Parameters

The run method has three parameters:

ngrams: is a set of strings representing every n-gram of the input matched by the Natex.
vars: is the variable dictionary, maintained by a DialogueFlow object, where the keys and values are variable names and objects corresponding to their values.
args: is a list of strings representing arguments specified in the macro call.

Let us modify the run method to see what ngrams and vars give:

def run(self, ngrams: Ngrams, vars: Dict[str, Any], args: List[Any]):
    print(ngrams.raw_text())
    print(ngrams.text())
    print(ngrams)
    print(vars)

#2: prints the original string of the matched input span before preprocessing.
#3: prints the input span, preprocessed by STDM and matched by the Natex.
#4: prints a set of n-grams.

When you interact with the the dialogue flow by running it (df.run()), it prints the followings:

S: Hello. What should I call you?
U: Dr. Jinho Choi
S: It's nice to meet you.

The raw_text method returns the original input:

Dr. Jinho Choi

The text method returns the preprocessed input used to match the Natex:

dr jinho choi

The ngrams gives a set of all possible n-grams in text():

{
    'dr',
    'jinho',
    'choi',
    'dr jinho',
    'jinho choi',
    'dr jinho choi'
}

Finally, the vars gives a dictionary consisting of both system-level and user-custom variables (no user-custom variables are saved at the moment):

{
    '__state__': '0',
    '__system_state__': 'start',
    '__stack__': [],
    '__user_utterance__': 'dr jinho choi',
    '__goal_return_state__': 'None',
    '__selected_response__': 'Hello. What should I call you?',
    '__raw_user_utterance__': 'Dr. Jinho Choi',
    '__converged__': 'True'
}

Implementation

Let us update the run method that matches the title, first name, and last name in the input and saves them to the variables $TITLE, $FIRSTNAME, and $LASTNAME, respectively:

def run(self, ngrams: Ngrams, vars: Dict[str, Any], args: List[Any]):
    r = re.compile(r"(mr|mrs|ms|dr)?(?:^|\s)([a-z']+)(?:\s([a-z']+))?")
    m = r.search(ngrams.text())
    if m is None: return False

    title, firstname, lastname = None, None, None
    
    if m.group(1):
        title = m.group(1)
        if m.group(3):
            firstname = m.group(2)
            lastname = m.group(3)
        else:
            firstname = m.group()
            lastname = m.group(2)
    else:
        firstname = m.group(2)
        lastname = m.group(3)

    vars['TITLE'] = title
    vars['FIRSTNAME'] = firstname
    vars['LASTNAME'] = lastname

    return True

#2: creates a regular expression to match the title, first name and last name.
#3: searches for the span to match.
#4: returns False if no match is found.
#6-18 -> exercise.
#20-22: saves the recognized title, first name, and last name to the corresponding variables.
#24: returns True as the regular expression matches the input span.

Given the updated macro, the above transitions can be modified as follow:

transitions = {
    'state': 'start',
    '`Hello. What should I call you?`': {
        '#GET_NAME': {
            '`It\'s nice to meet you,` $FIRSTNAME `.` $LASTNAME `is my favorite name.`': 'end'
        },
        'error': {
            '`Sorry, I didn\'t understand you.`': 'end'
        }
    }
}?

#5: uses the variables $FIRSTNAME and $LASTNAME retrieved by the macro to generate the output.

The followings show outputs:

S: Hello. What should I call you?
U: Dr. Jinho Choi
S: It's nice to meet you, jinho . choi is my favorite name.

S: Hello. What should I call you?
U: Jinho Choi
S: It's nice to meet you, jinho . choi is my favorite name.

S: Hello. What should I call you?
U: Dr. Choi
S: It's nice to meet you, dr choi . choi is my favorite name.

S: Hello. What should I call you?
U: Jinho
S: It's nice to meet you, jinho .  is my favorite name.

Although the last name is not recognized, and thus, it leaves a blank in the output, it is still considered "matched" because run() returns True for this case. Such output can be handled better by using the language generation capability in Natex.

Can macros be mixed with other Natex expressions?

Previous3.4. Regular Expression Next3.5. Quiz

Last updated 1 year ago