RedditSota / state-of-the-art-result-for-machine-learning-problems

This repository provides state of the art (SoTA) results for all machine learning problems. We do our best to keep this repository up to date. If you do find a problem's SoTA result is out of date or missing, please raise this as an issue or submit Google form (with this information: research paper name, dataset, metric, source code and year). We will fix it immediately.
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Include NLG papers #20

Open blakeperdue opened 6 years ago

blakeperdue commented 6 years ago

NLG, the other end of NLP, is important in many fields where AI is being applied. Please include the latest NLG research as well as imo it would be very helpful.

RedditSota commented 6 years ago

Can you please provide some help in NLG

Bachstelze commented 6 years ago

The recent results in natural language generation are stll heavily discussed. Some approaches to look at for general generation:

Improved Variational Autoencoders for Text Modeling using Dilated Convolutions https://github.com/kefirski/contiguous-succotash

Toward Controlled Generation of Text https://github.com/DSKSD/Controlled_Text_Generation https://github.com/GBLin5566/toward-controlled-generation-of-text-pytorch

Generating Sentences from a Continuous Space: https://github.com/Chung-I/Variational-Recurrent-Autoencoder-Tensorflow https://github.com/kefirski/pytorch_RVAE

A Hybrid Convolutional Variational Autoencoder for Text Generation https://github.com/stas-semeniuta/textvae

Neural Variational Inference for Text Processing https://github.com/carpedm20/variational-text-tensorflow

Language Generation with Recurrent Generative Adversarial Networks without Pre-training: https://github.com/amirbar/rnn.wgan

For sure there are other specific fields which correspondent with the generation field. Like paraphrasing:

Neural Paraphrase Generation with Stacked Residual LSTM Networks https://github.com/iamaaditya/neural-paraphrase-generation

Towards Universal Paraphrastic Sentence Embeddings https://github.com/jwieting/iclr2016

Paraphrase Generation with Deep Reinforcement Learning https://arxiv.org/abs/1711.00279

A Deep Generative Framework for Paraphrase Generation https://arxiv.org/abs/1709.05074

And error correction:

https://github.com/surmenok/DeepSpell_temp

https://github.com/nirbhayjm/sentence_grammar_correction

https://github.com/atpaino/deep-text-corrector docu: https://docs.floydhub.com/examples/deep_corrector/ blog: http://atpaino.com/2017/01/03/deep-text-correcter.html

recent papers for error correction: papers: https://arxiv.org/abs/1604.04677 https://arxiv.org/abs/1707.02026 https://arxiv.org/abs/1709.06429 https://arxiv.org/abs/1606.00189 https://arxiv.org/abs/1707.00299 https://sinaahmadi.github.io/files/[SinaAhmadi]Masters_thesis.pdf

for other nlp tasks checkout; https://github.com/Kyubyong/nlp_tasks

Bachstelze commented 6 years ago

and don't forget SeqGan: https://github.com/LantaoYu/SeqGAN

RedditSota commented 6 years ago

Thanks will update

On Sat, Nov 18, 2017 at 10:19 AM Bachstelze notifications@github.com wrote:

and don't forget SeqGan: https://github.com/LantaoYu/SeqGAN

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Bachstelze commented 6 years ago

Thanks will update

No problem. Did you get my email?

RedditSota commented 6 years ago

Yes. Thank you

On Sat, Nov 18, 2017 at 10:49 AM Bachstelze notifications@github.com wrote:

Thanks will update No problem. Did you get my email?

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Bachstelze commented 6 years ago

Learning Generic Sentence Representations Using Convolutional Neural Networks ... for multiple tasks like sentence reconstruction and future sentence prediction paper: https://arxiv.org/pdf/1611.07897.pdf code: https://github.com/zhegan27/ConvSent