A Beginner’s Guide to Natural Language Generation (NLG)

Natural Language Generation


Beyond interpreting human language input alone, modern artificial intelligence (AI) algorithms moreover demonstrate capabilities generating written or spoken responses like conversational chatbots delivering support capabilities autonomously without needing manual content scripts authored beforehand historically taxing creation workflows trying maintaining coherency at scale over lengthy dialogues. This natural language generation guide covers core concepts demystifying how computers architect coherent narrative responses seen reputably high performing generative algorithms captivating imaginations globally during recent few years with realistic human emulation abilities previously unfathomable practically before deep learning breakthroughs transformed language AI realizing more futuristic decision support use cases responsively.

Capabilities Powering Natural Language Generation

Several key methodologies power automated content creation capabilities:

  • Templates: Predefined text structures containing parameterized dynamic variables filled run-time like “Hello [[Name]], welcome your account portal will help managing services requested Friday [[Date]].” Enable rudimentary yet fast simple personalization many basic user interactions suffice using like account creation confirmations.
  • Machine Learning: Complex statistical models like deep neural networks ingest vast training corpora learning internal textual representations emulating patterns mimic human written styles thereafter used generating responses trained objectives. Requires lots data produce high quality but scales consistently massively. Learn more about Machine Learning here.
  • Rules-Based: Scripted logic scaffolds combine predefined grammatical rules and vocabulary orchestrating structured narrative flows altering generated passages introducing greater coherence and factual reliability through encoded human knowledge transparency design principles follow inspectably unlike black box neural models opaque deterministically still.

Key Natural Language Generation Tasks

Typical NLG use cases involve:

  • Chatbots: Generate conversational dialogue like customer service responding user questions or e-commerce shopping assistant navigating purchase decisions interactively.
  • Report Generation: Automate writing repetitive reporting documents like financial statements, sports summaries or patient health records using source data API feeds avoiding manual efforts.
  • Translation: Produce foreign language translations target languages using encoder-decoder neural models, dictionary mappings and grammatical transfer rules respectively.
  • Summarization: Synthesize lengthy content like research papers, news articles or meeting transcripts into concise high-level digestible overviews through algorithms extracting representative sentences illustrating key points.
  • Creative Writing: Assist human writers alleviating writers blocks suggesting relevant ideas, descriptive phrases and story continuations although pure literary art remains elusive still limited by emotional imagination constraints.

Architectural Components

Multiple modules orchestrate underlying:

  1. Domain Model: Structures knowledge scope like e-commerce catalog attributes, healthcare terminology or product specifications used grounding vocabulary.
  2. Text Planner: Outlines intended narrative flow coherent fuller passages whether business reports, tweet length responses or conversational dialogues.
  3. Sentence Generator: Constructs grammatically correct sentences structured previous steps intentions later.
  4. Context Tracker: Maintains dialogue state awareness adjusting responses plans acknowledging previous interactions appropriately across successive responses.

Implementation Advice

Practical guidance setting reasonable expectations include:

  1. Validate use case viability: Assess generated output quality sufficiency delivering intended user value confirming NLG sustainable further investment measured before committing deeper given technology immaturity integration complexity still taxes resources significantly being understood like most transformative technologies initially were decades prior serverless cloud maturity eventual mainstream ubiquity enjoyed today emerged innovated yesterday decades past forgotten already.
  2. Engineer for iterability: Scale initial minimum viable products conservatively meeting current quality bars widening use cases carefully as datasets and models improve thereafter avoiding overpromising unverified technological capabilities long term.
  3. Stress test extensively: Rigorously evaluate production safeguard rails resilience confirming policies and alert triggers maintaining quality bars expected after assessing real world unknowns realized post launch necessarily.
  4. Plan monitoring diligently: Monitor continuously model drifting detecting drops corpus updated or vocabulary shifts skewing generations away acceptable ranges flagging data deficiencies needing remediation.


Natural language generation brings conversational user experiences once exclusively human realm into scalable software reliance daily assisting global workforce through intelligently generated document drafts, conversational dialogues and contextual personalizations exceeding manually intensive efforts enterprises depended manual means alone before now innovated past intelligently finally by teams courageously leading change unafraid exploring barely tapped technological frontiers benefiting users and employees equally today through AI automation advantages maturing daily further tomorrow in ways only limited by imaginations dared dream creatively.

Share this content:

Post Comment