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Machine Translation

651 papers with code · Natural Language Processing

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

( Image credit: Google seq2seq )

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Greatest papers with code

Attention Is All You Need

NeurIPS 2017 tensorflow/models

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.

CONSTITUENCY PARSING MACHINE TRANSLATION

Exploiting Similarities among Languages for Machine Translation

17 Sep 2013tensorflow/models

Dictionaries and phrase tables are the basis of modern statistical machine translation systems.

MACHINE TRANSLATION

Semi-Supervised Sequence Modeling with Cross-View Training

EMNLP 2018 tensorflow/models

We therefore propose Cross-View Training (CVT), a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data.

CCG SUPERTAGGING DEPENDENCY PARSING MACHINE TRANSLATION MULTI-TASK LEARNING NAMED ENTITY RECOGNITION PART-OF-SPEECH TAGGING UNSUPERVISED REPRESENTATION LEARNING

Can Active Memory Replace Attention?

NeurIPS 2016 tensorflow/models

Several mechanisms to focus attention of a neural network on selected parts of its input or memory have been used successfully in deep learning models in recent years.

IMAGE CAPTIONING MACHINE TRANSLATION

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

ACL 2020 huggingface/transformers

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.

#9 best model for Question Answering on SQuAD1.1 dev (F1 metric)

DENOISING MACHINE TRANSLATION NATURAL LANGUAGE INFERENCE QUESTION ANSWERING TEXT GENERATION

Language Models are Unsupervised Multitask Learners

Preprint 2019 huggingface/transformers

Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific datasets.

 SOTA for Language Modelling on Text8 (using extra training data)

COMMON SENSE REASONING DOCUMENT SUMMARIZATION LANGUAGE MODELLING MACHINE TRANSLATION QUESTION ANSWERING READING COMPREHENSION TEXT GENERATION

Cross-lingual Language Model Pretraining

NeurIPS 2019 huggingface/transformers

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 UNSUPERVISED MACHINE TRANSLATION

Phrase-Based & Neural Unsupervised Machine Translation

EMNLP 2018 huggingface/transformers

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

Memory Efficient Adaptive Optimization

NeurIPS 2019 google-research/google-research

Adaptive gradient-based optimizers such as Adagrad and Adam are crucial for achieving state-of-the-art performance in machine translation and language modeling.

LANGUAGE MODELLING MACHINE TRANSLATION