AI for Natural Language Generation

100 min

Natural Language Generation (NLG) creates human-like text from structured data, enabling machines to communicate in natural language. NLG systems can generate reports, summaries, stories, dialogue, and more. NLG is the opposite of Natural Language Understanding (NLU)—while NLU extracts meaning from text, NLG creates text from meaning. Understanding NLG enables you to build systems that communicate naturally. Modern NLG has achieved remarkable fluency, though challenges remain in coherence, factuality, and control.

Modern NLG uses transformer models like GPT (Generative Pre-trained Transformer), BERT (for understanding context), and T5 (Text-to-Text Transfer Transformer) for advanced text generation. GPT models are autoregressive—they generate text token by token, predicting the next token based on previous tokens. T5 frames all tasks as text-to-text problems. These models are pre-trained on massive text corpora and can be fine-tuned for specific tasks. Understanding modern NLG models enables you to use state-of-the-art text generation. Transformer models have revolutionized NLG quality.

Applications include chatbots (conversational AI), content creation (articles, stories, marketing copy), automated reporting (summarizing data, generating reports), translation, and creative writing. Chatbots use NLG to respond to users naturally. Content creation AI can generate articles, stories, and marketing materials. Automated reporting summarizes data into readable reports. Understanding NLG applications enables you to identify opportunities. NLG is becoming increasingly common in business and creative applications.

NLG requires careful prompt engineering (crafting inputs to guide generation) and output validation (ensuring quality and accuracy). Prompt engineering involves designing inputs that guide models to produce desired outputs. Effective prompts can dramatically improve output quality. Output validation checks for accuracy, coherence, appropriateness, and factuality. Understanding prompt engineering and validation enables you to build effective NLG systems. NLG outputs should always be validated, especially for factual content.

Challenges in NLG include maintaining coherence (ensuring text makes sense), factuality (ensuring generated facts are accurate), control (ensuring outputs match requirements), and bias (ensuring outputs are fair and appropriate). Coherence requires models to maintain context and logical flow. Factuality is critical for applications like reporting. Control ensures outputs match specific requirements. Bias can appear in generated text, requiring careful monitoring. Understanding NLG challenges enables you to address them effectively.

Best practices include using appropriate models for your task, crafting effective prompts, validating outputs carefully, monitoring for bias and inappropriate content, and understanding model limitations. Understanding NLG enables you to build systems that generate natural, useful text. NLG is powerful but requires careful use to ensure quality and appropriateness. Modern NLG models are tools that require human oversight and validation.

Key Concepts

NLG creates human-like text from structured data.
Modern NLG uses transformer models (GPT, BERT, T5).
NLG applications include chatbots, content creation, and reporting.
Prompt engineering and output validation are essential.
NLG faces challenges in coherence, factuality, and control.

Learning Objectives

Master

Understanding NLG concepts and applications
Using transformer models for text generation
Implementing prompt engineering techniques
Validating and controlling NLG outputs

Develop

NLG thinking
Understanding text generation challenges
Designing effective NLG systems

Tips

Craft effective prompts to guide model outputs.
Always validate NLG outputs, especially for factual content.
Use appropriate models for your specific task.
Monitor for bias and inappropriate content in generated text.

Common Pitfalls

Not validating outputs, generating inaccurate or inappropriate text.
Poor prompt engineering, getting undesired outputs.
Trusting generated text without verification, especially for facts.
Not monitoring for bias, generating unfair or harmful content.

Summary

NLG creates human-like text from structured data.
Modern NLG uses transformer models for high-quality generation.
NLG applications include chatbots, content creation, and reporting.
Prompt engineering and validation are essential for effective NLG.
Understanding NLG enables building systems that communicate naturally.

Exercise

Implement a simple text generation system using Markov chains and templates.

import random
import re
from collections import defaultdict

class TextGenerator:
    def __init__(self):
        self.markov_chain = defaultdict(list)
        self.templates = []
        
    def train_on_text(self, text, order=2):
        """Train Markov chain on text"""
        words = text.split()
        
        for i in range(len(words) - order):
            key = tuple(words[i:i + order])
            next_word = words[i + order]
            self.markov_chain[key].append(next_word)
    
    def add_template(self, template):
        """Add a template for structured text generation"""
        self.templates.append(template)
    
    def generate_markov_text(self, start_words, length=50):
        """Generate text using Markov chain"""
        if len(start_words) < 2:
            start_words = ["The", "quick"]
        
        current = tuple(start_words[-2:])
        result = list(current)
        
        for _ in range(length):
            if current in self.markov_chain:
                next_word = random.choice(self.markov_chain[current])
                result.append(next_word)
                current = tuple(result[-2:])
            else:
                break
        
        return " ".join(result)
    
    def generate_from_template(self, data):
        """Generate text from template and data"""
        if not self.templates:
            return "No templates available"
        
        template = random.choice(self.templates)
        
        # Simple template substitution
        for key, value in data.items():
            template = template.replace(f"{{{key}}}", str(value))
        
        return template
    
    def generate_weather_report(self, weather_data):
        """Generate a weather report"""
        templates = [
            "Today's weather in {city} is {condition} with a temperature of {temp}°F.",
            "The forecast for {city} shows {condition} weather and {temp}°F.",
            "In {city}, expect {condition} conditions with temperatures around {temp}°F."
        ]
        
        # Add weather-specific templates
        for template in templates:
            self.add_template(template)
        
        return self.generate_from_template(weather_data)

# Training data
sample_text = """
The quick brown fox jumps over the lazy dog. The fox is quick and brown. 
The dog is lazy and sleeps all day. The fox likes to jump and play. 
The dog prefers to rest and watch. Both animals live in the forest.
"""

# Initialize generator
generator = TextGenerator()
generator.train_on_text(sample_text)

# Generate text
print("Markov Chain Generated Text:")
print(generator.generate_markov_text(["The", "fox"], 20))
print()

# Template-based generation
weather_data = {
    "city": "New York",
    "condition": "sunny",
    "temp": 75
}

print("Template-based Weather Report:")
print(generator.generate_weather_report(weather_data))
print()

# More complex template system
class AdvancedTextGenerator:
    def __init__(self):
        self.sentence_patterns = [
            "The {subject} {action} {object}.",
            "{subject} is {adjective} and {action}.",
            "When {condition}, {subject} {action}.",
            "The {adjective} {subject} {action} {object}."
        ]
        
        self.vocabulary = {
            "subject": ["cat", "dog", "bird", "fish", "robot", "computer"],
            "action": ["runs", "jumps", "swims", "flies", "processes", "calculates"],
            "object": ["ball", "tree", "water", "sky", "data", "information"],
            "adjective": ["quick", "smart", "efficient", "powerful", "intelligent"],
            "condition": ["it's sunny", "it rains", "the system is active", "data is available"]
        }
    
    def generate_sentence(self):
        """Generate a random sentence from patterns"""
        pattern = random.choice(self.sentence_patterns)
        
        # Replace placeholders with random words
        for key, words in self.vocabulary.items():
            placeholder = "{" + key + "}"
            if placeholder in pattern:
                pattern = pattern.replace(placeholder, random.choice(words))
        
        return pattern
    
    def generate_paragraph(self, num_sentences=5):
        """Generate a paragraph of sentences"""
        sentences = []
        for _ in range(num_sentences):
            sentence = self.generate_sentence()
            sentences.append(sentence)
        
        return " ".join(sentences)

# Advanced generator
advanced_gen = AdvancedTextGenerator()

print("Advanced Template Generation:")
print(advanced_gen.generate_paragraph(3))
print()

# NLG best practices
print("NLG Best Practices:")
print("1. Template Diversity: Use multiple templates for variety")
print("2. Context Awareness: Consider context when generating text")
print("3. Quality Control: Validate generated text for accuracy")
print("4. Personalization: Adapt output to user preferences")
print("5. Ethical Considerations: Ensure generated content is appropriate")