Text Generation
Text generation is the task of generating text with the goal of appearing indistinguishable to human-written text.
State-of-the-art leaderboards
| Trend | Dataset | Best Method | Paper title | Paper | Code | Compare |
|---|---|---|---|---|---|---|
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
Subtasks
Greatest papers with code
MaskGAN: Better Text Generation via Filling in the______
Additionally, these models are typically trained via maxi- mum likelihood and teacher forcing.
#3 best model for
Multivariate Time Series Imputation
on Basketball Players Movement
Generating Sequences With Recurrent Neural Networks
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.
fairseq: A Fast, Extensible Toolkit for Sequence Modeling
NAACL 2019
• facebookresearch/fairseq-py
• 
fairseq is an open-source sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling, and other text generation tasks.
Mixture Models for Diverse Machine Translation: Tricks of the Trade
• pytorch/fairseq
•
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
• facebookresearch/fairseq-py
•
We observe that our method consistently outperforms BS and previously proposed techniques for diverse decoding from neural sequence models.
IMAGE CAPTIONING MACHINE TRANSLATION QUESTION GENERATION TEXT GENERATION TIME SERIES
Sequence-to-Sequence Learning as Beam-Search Optimization
EMNLP 2016
• facebookresearch/fairseq-py
•
In this work, we introduce a model and beam-search training scheme, based on the work of Daume III and Marcu (2005), that extends seq2seq to learn global sequence scores.
#16 best model for
Machine Translation
on IWSLT2015 German-English
Show and Tell: A Neural Image Caption Generator
CVPR 2015
• karpathy/neuraltalk
• 
Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions.
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
• LantaoYu/SeqGAN
• 
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.
#2 best model for
Text Generation
on EMNLP2017 WMT
Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation
ACL 2019
• asyml/texar
•
The versatile toolkit also fosters technique sharing across different text generation tasks.
Hierarchical Text Generation and Planning for Strategic Dialogue
ICML 2018
• facebookresearch/end-to-end-negotiator
•
End-to-end models for goal-orientated dialogue are challenging to train, because linguistic and strategic aspects are entangled in latent state vectors.