Machine Translation

1114 papers with code • 55 benchmarks • 50 datasets

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

( Image credit: Google seq2seq )

Greatest papers with code

Leveraging Pre-trained Checkpoints for Sequence Generation Tasks

huggingface/transformers TACL 2020

Unsupervised pre-training of large neural models has recently revolutionized Natural Language Processing.

Machine Translation Natural Language Understanding +3

Facebook FAIR's WMT19 News Translation Task Submission

huggingface/transformers WS 2019

This paper describes Facebook FAIR's submission to the WMT19 shared news translation task.

Machine Translation

Language Models are Unsupervised Multitask Learners

huggingface/transformers Preprint 2019

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)

Common Sense Reasoning Data-to-Text Generation +6

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.

Language Modelling Natural Language Understanding +1

Phrase-Based & Neural Unsupervised Machine Translation

huggingface/transformers 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.

Unsupervised Machine Translation

Pre-trained Summarization Distillation

huggingface/transformers 24 Oct 2020

A third, simpler approach is to 'shrink and fine-tune' (SFT), which avoids any explicit distillation by copying parameters to a smaller student model and then fine-tuning.

Knowledge Distillation Machine Translation

Felix: Flexible Text Editing Through Tagging and Insertion

google-research/google-research Findings of the Association for Computational Linguistics 2020

We achieve this by decomposing the text-editing task into two sub-tasks: tagging to decide on the subset of input tokens and their order in the output text and insertion to in-fill the missing tokens in the output not present in the input.

Automatic Post-Editing Language Modelling +2

Scalable Second Order Optimization for Deep Learning

google-research/google-research 20 Feb 2020

Optimization in machine learning, both theoretical and applied, is presently dominated by first-order gradient methods such as stochastic gradient descent.

Image Classification Language Modelling +1

Meta Back-translation

google-research/google-research ICLR 2021

Back-translation is an effective strategy to improve the performance of Neural Machine Translation~(NMT) by generating pseudo-parallel data.

Machine Translation Meta-Learning

AutoDropout: Learning Dropout Patterns to Regularize Deep Networks

google-research/google-research 5 Jan 2021

As a result, these conventional methods are less effective than methods that leverage the structures, such as SpatialDropout and DropBlock, which randomly drop the values at certain contiguous areas in the hidden states and setting them to zero.

Image Classification Language Modelling +1