Tools for Augmenting Data and Generating Texts

Text Generation Models

Text generation models are trained on large corpora and can generate semantically coherent and lexically varied sentences.

• GPT-3/GPT-4: Generates fluent and contextually relevant text. Supports fine-tuning and controlled outputs through prompt engineering.
• GPT-2: Effective for text generation and fine-tuning to produce specific semantic or syntactic structures.
• T5 (Text-to-Text Transfer Transformer): Handles tasks by converting input into text-to-text format, generating sentences from structured input.
• BERT: Primarily for understanding tasks, but paired with generation heads like BART or T5, it can generate relevant sentences.

Text Augmentation Libraries

Libraries for NLP data augmentation by altering existing sentences while retaining their meaning.

• nlpaug: Augments text by synonym replacement, random insertions, and transformations using word embeddings.
  • PIP reference
  • Post: Data Augmentation library for text
  • nlpaug.readthedocs.io
• TextAttack: Provides adversarial attacks and paraphrasing methods for generating variations of sentences.
  • TextAttack Documentation

Semantic Structure-based Text Generation

Tools for precise control over semantic structure in generated sentences.

• Controlled Text Generation (via GPT-3/4): Uses structured prompts to guide text generation toward desired structures or concepts.
• OpenAI Codex: Generates text based on semantic instructions or structural descriptions.
• DeepAI’s Text Generation API: Generates text based on input semantics and structure.
• CTRL (Conditional Transformer Language Model): Conditions text generation on control codes for specific topics or structures.

Rule-based Text Generation

Generates text based on predefined templates or rules.

• OpenNLP: Uses templates and rules for sentence generation.
  • Apache OpenNLP Developer Documentation
• Template-based Generation: Tools like Yarn or Jinja2 allow for generating text using templates with placeholders.

Lexical Substitution and Paraphrasing Tools

Tools for modifying words or phrases while maintaining semantic meaning.

• Paraphrase Generation with BART or T5: Generates sentence variations that preserve meaning.
• WordNet-based tools: Uses lexical substitution to replace words with synonyms or semantically related words.

Generating Text Based on Semantic Structures

Tools and techniques to guide text generation using semantic roles or lexical features.

• Graph-based Models: Represent word or sentence relationships to guide generation using tools like spaCy.
• Semantic Role Labeling (SRL): Tools like AllenNLP tag sentence components to generate text following specific roles or patterns.
  • Read more about Semantic Role Labeling in paper: PriMeSRL-Eval: A Practical Quality Metric for Semantic Role Labeling Systems Evaluation
• Lexical and Syntactic Features: Fine-tunes models to control lexical variety or syntactic structure based on desired patterns.

Conclusion

A wide range of tools like GPT-3, T5, TextAttack, and nlpaug are available for augmenting data and generating text. They provide flexibility for creating semantically diverse and lexically varied text, while specialized tools like AllenNLP enable controlled generation based on specific structures and constraints.