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Unsupervised Machine Translation

13 papers with code · Natural Language Processing
Subtask of Machine Translation

Unsupervised machine translation is the task of doing machine translation without any translation resources at training time.

( Image credit: Phrase-Based & Neural Unsupervised Machine Translation )

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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

Unsupervised Machine Translation Using Monolingual Corpora Only

ICLR 2018 facebookresearch/MUSE

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

UNSUPERVISED MACHINE TRANSLATION

Word Translation Without Parallel Data

ICLR 2018 facebookresearch/MUSE

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.

UNSUPERVISED MACHINE TRANSLATION WORD ALIGNMENT WORD EMBEDDINGS

Language Models are Few-Shot Learners

28 May 2020openai/gpt-3

By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.

 SOTA for Language Modelling on Penn Treebank (Word Level) (using extra training data)

COMMON SENSE REASONING COREFERENCE RESOLUTION DOMAIN ADAPTATION FEW-SHOT LEARNING LANGUAGE MODELLING NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SENTENCE COMPLETION UNSUPERVISED MACHINE TRANSLATION WORD SENSE DISAMBIGUATION

Cross-lingual Language Model Pretraining

NeurIPS 2019 facebookresearch/XLM

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

Unsupervised Neural Machine Translation

ICLR 2018 rsennrich/subword-nmt

In spite of the recent success of neural machine translation (NMT) in standard benchmarks, the lack of large parallel corpora poses a major practical problem for many language pairs.

UNSUPERVISED MACHINE TRANSLATION

MASS: Masked Sequence to Sequence Pre-training for Language Generation

7 May 2019microsoft/MASS

Pre-training and fine-tuning, e. g., BERT, have achieved great success in language understanding by transferring knowledge from rich-resource pre-training task to the low/zero-resource downstream tasks.

CONVERSATIONAL RESPONSE GENERATION TEXT GENERATION TEXT SUMMARIZATION UNSUPERVISED MACHINE TRANSLATION

Unsupervised Statistical Machine Translation

EMNLP 2018 artetxem/vecmap

While modern machine translation has relied on large parallel corpora, a recent line of work has managed to train Neural Machine Translation (NMT) systems from monolingual corpora only (Artetxe et al., 2018c; Lample et al., 2018).

LANGUAGE MODELLING UNSUPERVISED MACHINE TRANSLATION

Incorporating BERT into Neural Machine Translation

ICLR 2020 bert-nmt/bert-nmt

While BERT is more commonly used as fine-tuning instead of contextual embedding for downstream language understanding tasks, in NMT, our preliminary exploration of using BERT as contextual embedding is better than using for fine-tuning.

NATURAL LANGUAGE UNDERSTANDING READING COMPREHENSION TEXT CLASSIFICATION UNSUPERVISED MACHINE TRANSLATION