Text Generation Edit Task
Natural Language Processing • Action Generation
About
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 )
Benchmarks
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Subtasks
Datasets
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.
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
NeurIPS 2020
• huggingface/transformers
• 
Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks.
Ranked #6 on
Question Answering
on Natural Questions (short)
Plug and Play Language Models: A Simple Approach to Controlled Text Generation
ICLR 2020
• huggingface/transformers
•
Large transformer-based language models (LMs) trained on huge text corpora have shown unparalleled generation capabilities.
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
ACL 2020
• huggingface/transformers
•
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.
Ranked #2 on
Text Summarization
on X-Sum
ABSTRACTIVE TEXT SUMMARIZATION DENOISING MACHINE TRANSLATION NATURAL LANGUAGE INFERENCE QUESTION ANSWERING TEXT GENERATION
HuggingFace's Transformers: State-of-the-art Natural Language Processing
Transformer architectures have facilitated building higher-capacity models and pretraining has made it possible to effectively utilize this capacity for a wide variety of tasks.
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.
Ranked #1 on
Language Modelling
on enwik8
(using extra training data)
4 COMMON SENSE REASONING DATA-TO-TEXT GENERATION DOCUMENT SUMMARIZATION LANGUAGE MODELLING MACHINE TRANSLATION MULTI-TASK LEARNING QUESTION ANSWERING READING COMPREHENSION
Towards Automatic Evaluation of Dialog Systems: A Model-Free Off-Policy Evaluation Approach
• google-research/google-research
• 
An ideal environment for evaluating dialog systems, also known as the Turing test, needs to involve human interaction, which is usually not affordable for large-scale experiments.
Stepwise Extractive Summarization and Planning with Structured Transformers
EMNLP 2020
• google-research/google-research
•
We propose encoder-centric stepwise models for extractive summarization using structured transformers -- HiBERT and Extended Transformers.
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.
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