Text Generation

823 papers with code • 158 benchmarks • 100 datasets

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:

( Image credit: Adversarial Ranking for Language Generation )

Libraries

Use these libraries to find Text Generation models and implementations
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199
3 papers
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Most implemented papers

Show and Tell: A Neural Image Caption Generator

karpathy/neuraltalk CVPR 2015

Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions.

Generating Sequences With Recurrent Neural Networks

karpathy/char-rnn 4 Aug 2013

This paper shows how Long Short-term Memory recurrent neural networks can be used to generate complex sequences with long-range structure, simply by predicting one data point at a time.

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

huggingface/transformers ACL 2020

We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token.

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

LantaoYu/SeqGAN 18 Sep 2016

As a new way of training generative models, Generative Adversarial Nets (GAN) that uses a discriminative model to guide the training of the generative model has enjoyed considerable success in generating real-valued data.

Learning Transferable Visual Models From Natural Language Supervision

openai/CLIP 26 Feb 2021

State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories.

Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models

ashwinkalyan/dbs 7 Oct 2016

We observe that our method consistently outperforms BS and previously proposed techniques for diverse decoding from neural sequence models.

BERTScore: Evaluating Text Generation with BERT

Tiiiger/bert_score ICLR 2020

We propose BERTScore, an automatic evaluation metric for text generation.

Language Models are Unsupervised Multitask Learners

PaddlePaddle/PaddleNLP Preprint 2019

Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets.

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

microsoft/unilm NeurIPS 2019

This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks.

MASS: Masked Sequence to Sequence Pre-training for Language Generation

microsoft/MASS 7 May 2019

Pre-training and fine-tuning, e. g., BERT, have achieved great success in language understanding by transferring knowledge from rich-resource pre-training task to the low/zero-resource downstream tasks.