Translation
3182 papers with code • 7 benchmarks • 15 datasets
Benchmarks
These leaderboards are used to track progress in Translation
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Libraries
Use these libraries to find Translation models and implementationsDatasets
WMT 2016
xP3
CVSS
Oxford Radar RobotCar Dataset
Duolingo STAPLE Shared Task
Demetr
SpeechMatrix
ACES
FRMT
Most implemented papers
Spatial Transformer Networks
tensorpack/tensorpack
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NeurIPS 2015
Convolutional Neural Networks define an exceptionally powerful class of models, but are still limited by the lack of ability to be spatially invariant to the input data in a computationally and parameter efficient manner.
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
In this paper, we propose a novel neural network model called RNN Encoder-Decoder that consists of two recurrent neural networks (RNN).
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
huggingface/transformers
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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.
Convolutional Sequence to Sequence Learning
facebookresearch/fairseq
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ICML 2017
The prevalent approach to sequence to sequence learning maps an input sequence to a variable length output sequence via recurrent neural networks.
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
eriklindernoren/PyTorch-GAN
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CVPR 2018
To address this limitation, we propose StarGAN, a novel and scalable approach that can perform image-to-image translations for multiple domains using only a single model.
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
locuslab/TCN
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Our results indicate that a simple convolutional architecture outperforms canonical recurrent networks such as LSTMs across a diverse range of tasks and datasets, while demonstrating longer effective memory.
Autoencoding beyond pixels using a learned similarity metric
clementchadebec/benchmark_VAE
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We present an autoencoder that leverages learned representations to better measure similarities in data space.
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
NVIDIA/DeepLearningExamples
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To improve parallelism and therefore decrease training time, our attention mechanism connects the bottom layer of the decoder to the top layer of the encoder.
U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation
We propose a novel method for unsupervised image-to-image translation, which incorporates a new attention module and a new learnable normalization function in an end-to-end manner.
Neural Machine Translation of Rare Words with Subword Units
Neural machine translation (NMT) models typically operate with a fixed vocabulary, but translation is an open-vocabulary problem.
