Natural Language Processing
 
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Machine Translation

2154 papers with code • 80 benchmarks • 77 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
29,292
huggingface/transformers
14 papers
125,385
tensorflow/tensor2tensor
10 papers
14,913
See all 14 libraries.

Datasets

Subtasks

Most implemented papers

Most implemented Social Latest No 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

Phrase-Based & Neural Unsupervised Machine Translation

facebookresearch/UnsupervisedMT • • EMNLP 2018

Machine translation systems achieve near human-level performance on some languages, yet their effectiveness strongly relies on the availability of large amounts of parallel sentences, which hinders their applicability to the majority of language pairs.

15
Paper
Code

QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension

BangLiu/QANet-PyTorch • • ICLR 2018

On the SQuAD dataset, our model is 3x to 13x faster in training and 4x to 9x faster in inference, while achieving equivalent accuracy to recurrent models.

15
Paper
Code

BERTScore: Evaluating Text Generation with BERT

Tiiiger/bert_score • • ICLR 2020

We propose BERTScore, an automatic evaluation metric for text generation.

15
Paper
Code

ResMLP: Feedforward networks for image classification with data-efficient training

facebookresearch/deit • • NeurIPS 2021

We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image classification.

15
Paper
Code

Attention-Based Models for Speech Recognition

Alexander-H-Liu/End-to-end-ASR-Pytorch • • NeurIPS 2015

Recurrent sequence generators conditioned on input data through an attention mechanism have recently shown very good performance on a range of tasks in- cluding machine translation, handwriting synthesis and image caption gen- eration.

14
Paper
Code

Tensor2Tensor for Neural Machine Translation

tensorflow/tensor2tensor • • WS 2018

Tensor2Tensor is a library for deep learning models that is well-suited for neural machine translation and includes the reference implementation of the state-of-the-art Transformer model.

14
Paper
Code

FNet: Mixing Tokens with Fourier Transforms

google-research/google-research • • NAACL 2022

At longer input lengths, our FNet model is significantly faster: when compared to the "efficient" Transformers on the Long Range Arena benchmark, FNet matches the accuracy of the most accurate models, while outpacing the fastest models across all sequence lengths on GPUs (and across relatively shorter lengths on TPUs).

14
Paper
Code

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

tensorflow/lingvo • • NeurIPS 2019

Scaling up deep neural network capacity has been known as an effective approach to improving model quality for several different machine learning tasks.

13
Paper
Code

Massive Exploration of Neural Machine Translation Architectures

google/seq2seq • • EMNLP 2017

Neural Machine Translation (NMT) has shown remarkable progress over the past few years with production systems now being deployed to end-users.

12
Paper
Code
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