Natural Language Processing
 
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Machine Translation

2148 papers with code • 78 benchmarks • 76 datasets

Machine translation is the task of translating a sentence in a source language to a different target language.

Approaches for machine translation can range from rule-based to statistical to neural-based. More recently, encoder-decoder attention-based architectures like BERT have attained major improvements in machine translation.

One of the most popular datasets used to benchmark machine translation systems is the WMT family of datasets. Some of the most commonly used evaluation metrics for machine translation systems include BLEU, METEOR, NIST, and others.

( Image credit: Google seq2seq )

Libraries

Use these libraries to find Machine Translation models and implementations
awslabs/sockeye
24 papers
1,206
pytorch/fairseq
15 papers
29,192
huggingface/transformers
14 papers
124,527
tensorflow/tensor2tensor
10 papers
14,865
See all 14 libraries.

Datasets

Subtasks

Most implemented papers

Most implemented Social Latest No code

3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

wolny/pytorch-3dunet • • 21 Jun 2016

This paper introduces a network for volumetric segmentation that learns from sparsely annotated volumetric images.

26
Paper
Code

Neural Machine Translation of Rare Words with Subword Units

rsennrich/subword-nmt • ACL 2016

Neural machine translation (NMT) models typically operate with a fixed vocabulary, but translation is an open-vocabulary problem.

25
Paper
Code

Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models

ashwinkalyan/dbs • • 7 Oct 2016

We observe that our method consistently outperforms BS and previously proposed techniques for diverse decoding from neural sequence models.

25
Paper
Code

Recurrent Neural Network Regularization

wojzaremba/lstm • 8 Sep 2014

We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units.

21
Paper
Code

On the Variance of the Adaptive Learning Rate and Beyond

LiyuanLucasLiu/RAdam • • ICLR 2020

The learning rate warmup heuristic achieves remarkable success in stabilizing training, accelerating convergence and improving generalization for adaptive stochastic optimization algorithms like RMSprop and Adam.

21
Paper
Code

Word Translation Without Parallel Data

facebookresearch/MUSE • • ICLR 2018

We finally describe experiments on the English-Esperanto low-resource language pair, on which there only exists a limited amount of parallel data, to show the potential impact of our method in fully unsupervised machine translation.

19
Paper
Code

Lookahead Optimizer: k steps forward, 1 step back

michaelrzhang/lookahead • • NeurIPS 2019

The vast majority of successful deep neural networks are trained using variants of stochastic gradient descent (SGD) algorithms.

19
Paper
Code

Sockeye: A Toolkit for Neural Machine Translation

awslabs/sockeye • • 15 Dec 2017

Written in Python and built on MXNet, the toolkit offers scalable training and inference for the three most prominent encoder-decoder architectures: attentional recurrent neural networks, self-attentional transformers, and fully convolutional networks.

16
Paper
Code

Cross-lingual Language Model Pretraining

huggingface/transformers • • NeurIPS 2019

On unsupervised machine translation, we obtain 34. 3 BLEU on WMT'16 German-English, improving the previous state of the art by more than 9 BLEU.

16
Paper
Code

Unsupervised Machine Translation Using Monolingual Corpora Only

facebookresearch/MUSE • • ICLR 2018

By learning to reconstruct in both languages from this shared feature space, the model effectively learns to translate without using any labeled data.

15
Paper
Code
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