Text Generation
Text generation is the task of generating text with the goal of appearing indistinguishable to human-written text.
( Image credit: Adversarial Ranking for Language Generation )
Leaderboards
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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
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
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.
#9 best model for
Question Answering
on SQuAD1.1 dev
(F1 metric)
DENOISING MACHINE TRANSLATION NATURAL LANGUAGE INFERENCE QUESTION ANSWERING TEXT GENERATION
HuggingFace's Transformers: State-of-the-art Natural Language Processing
In this paper, we present HuggingFace's Transformers library, a library for state-of-the-art NLP, making these developments available to the community by gathering state-of-the-art general-purpose pretrained models under a unified API together with an ecosystem of libraries, examples, tutorials and scripts targeting many downstream NLP tasks.
CTRL: A Conditional Transformer Language Model for Controllable Generation
Preprint 2019
• huggingface/transformers
• 
Large-scale language models show promising text generation capabilities, but users cannot easily control particular aspects of the generated text.
Language Models are Unsupervised Multitask Learners
Preprint 2019
• huggingface/transformers
•
Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets.
SOTA for Language Modelling on Text8 (using extra training data)
COMMON SENSE REASONING DOCUMENT SUMMARIZATION LANGUAGE MODELLING MACHINE TRANSLATION QUESTION ANSWERING READING COMPREHENSION TEXT GENERATION
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.
Neural Assistant: Joint Action Prediction, Response Generation, and Latent Knowledge Reasoning
In this paper, we develop Neural Assistant: a single neural network model that takes conversation history and an external knowledge source as input and jointly produces both text response and action to be taken by the system as output.
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
• pytorch/fairseq
•
We observe that our method consistently outperforms BS and previously proposed techniques for diverse decoding from neural sequence models.
SOTA for Time Series on Amazon
IMAGE CAPTIONING MACHINE TRANSLATION QUESTION GENERATION TEXT GENERATION TIME SERIES
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.