Text Generation

Text generation is the task of generating text with the goal of appearing indistinguishable to human-written text. This task if more formally known as "natural language generation" in the literature. Text generation can be addressed with Markov processes or deep generative models like LSTMs. Recently, some of the most advanced methods for text generation include BART, GPT and other GAN-based approaches. Text generation systems are evaluated either through human ratings or automatic evaluation metrics like METEOR, ROUGE, and BLEU. Further readings: The survey: Text generation models in deep learning Modern Methods for Text Generation ( Image credit: Adversarial Ranking for Language Generation )

Benchmarks

Libraries

Datasets

Subtasks

Most implemented papers

Show and tell: A neural image caption generator

Content

Generating Sequences With Recurrent Neural Networks

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Learning Transferable Visual Models From Natural Language Supervision

Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

BERTScore: Evaluating Text Generation with BERT

Unified Language Model Pre-training for Natural Language Understanding and Generation

HuggingFace's Transformers: State-of-the-art Natural Language Processing