---
title: "Unsupervised Machine Translation Using — Lample 2017"
description: "Neural machine translation usually needs millions of aligned sentence pairs, which most low-resource language pairs lack."
authors: ["Guillaume Lample", "Alexis Conneau", "Ludovic Denoyer", "Marc'Aurelio Ranzato"]
year: 2017
venue: "arXiv:1711.00043"
source: "https://arxiv.org/abs/1711.00043"
doi: "10.48550/arxiv.1711.00043"
arxiv: "1711.00043"
licence: "arXiv — non-exclusive licence to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/lample-2017-unsupervised-machine-translation-using-monolingual/"
---
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# Unsupervised Machine Translation Using Monolingual Corpora Only

**Authors:** [Guillaume Lample](https://md.research.elamur.ai/free/en/authors/guillaume-lample/), [Alexis Conneau](https://md.research.elamur.ai/free/en/authors/alexis-conneau/), Ludovic Denoyer, [Marc'Aurelio Ranzato](https://md.research.elamur.ai/free/en/authors/marc-aurelio-ranzato/) · **Year:** 2017 · **Venue:** arXiv:1711.00043 · **Licence:** [arXiv — non-exclusive licence to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1711.00043) · [PDF](https://arxiv.org/pdf/1711.00043) · [DOI 10.48550/arxiv.1711.00043](https://doi.org/10.48550/arxiv.1711.00043) · [arXiv 1711.00043](https://arxiv.org/abs/1711.00043) · [OpenAlex](https://openalex.org/W2765961751)

**Citations:** 173 citations (openalex-snapshot · 2026-10-14) · [477](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Neural machine translation usually needs millions of aligned sentence pairs, which most low-resource language pairs lack. This paper tests whether a translator can be trained with no parallel data, using only one monolingual corpus per language. The authors use a single attentional LSTM encoder–decoder for both languages. Only the word embeddings are language-specific, so sentences from either language are encoded into a common latent space. Training combines two reconstruction tasks. In the first, the model restores a sentence from a corrupted copy of itself (denoising auto-encoding). In the second, it recovers a sentence from the translation produced by the previous model iteration (back-translation). An adversarial discriminator pushes the latent codes of both languages toward the same distribution. Training starts from word-by-word translation with a bilingual dictionary that was also learned without supervision. On Multi30k and WMT, the English–French system reaches 32.8 and 15.1 BLEU. The work shows that usable translation can be learned from monolingual text alone, which makes it relevant to languages with little or no parallel data.

## Key points
- Results: 32.76 BLEU on English–French Multi30k-Task1 (above 22 for every language pair there) and 15.1 BLEU on WMT English–French, with no parallel sentences used in training.
- On WMT, the unsupervised model performs about as well as a comparable supervised system trained on 100,000 sentence pairs.
- Architecture: 3-layer bidirectional LSTM encoder and 3-layer LSTM decoder with attention (Bahdanau et al.). LSTM layers and attention weights are shared across languages, embedding and hidden sizes are 300, and output uses greedy decoding.
- Noise for denoising: words are randomly dropped with probability p_wd, and word order is lightly shuffled so that no token moves more than k positions.
- The first translation model comes from an unsupervised bilingual lexicon (Conneau et al., 2017, "Word translation without parallel data"). Each later iteration retrains on fresh back-translations from the previous model.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Other](https://md.research.elamur.ai/free/en/shelves/other/)

Topics: [Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/)

## Cited by

Cited by

477 papers of our library cite it (of the 140673 whose bibliographies we read); 39 of them have a page here, 438 not yet.

- [A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation](https://md.research.elamur.ai/free/en/papers/adelani-2022-few-thousand-translations-go-long/)
- [Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges](https://md.research.elamur.ai/free/en/papers/arivazhagan-2019-massively-multilingual-neural-machine-translation/)
- [Unsupervised Statistical Machine Translation](https://md.research.elamur.ai/free/en/papers/artetxe-2018-unsupervised-statistical-machine-translation/)
- [An Effective Approach to Unsupervised Machine Translation](https://md.research.elamur.ai/free/en/papers/artetxe-2019-effective-approach-unsupervised-machine-translation/)
- [ProofNet: Autoformalizing and Formally Proving Undergraduate-Level Mathematics](https://md.research.elamur.ai/free/en/papers/azerbayev-2023-proofnet-autoformalizing-formally-proving-undergraduate/)
- [Evaluating prose style transfer with the Bible](https://md.research.elamur.ai/free/en/papers/carlson-2017-evaluating-prose-style-transfer-bible/)
- [Language ID in the Wild: Unexpected Challenges on the Path to a Thousand-Language Web Text Corpus](https://md.research.elamur.ai/free/en/papers/caswell-2020-language-id-wild-unexpected/)
- [An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://md.research.elamur.ai/free/en/papers/chen-2023-empirical-survey-data-augmentation-limited/)
- [AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages](https://md.research.elamur.ai/free/en/papers/ebrahimi-2022-americasnli-evaluating-zero-shot-natural/)
- [APE at Scale and Its Implications on MT Evaluation Biases](https://md.research.elamur.ai/free/en/papers/freitag-2019-ape-scale-its-implications-mt/)
- [ImageBind: One Embedding Space To Bind Them All](https://md.research.elamur.ai/free/en/papers/girdhar-2023-imagebind-one-embedding-space-bind/)
- [Meta-Learning for Low-Resource Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/gu-2018-meta-learning-low-resource-neural/)
- [Universal Neural Machine Translation for Extremely Low Resource Languages](https://md.research.elamur.ai/free/en/papers/gu-2018-universal-neural-machine-translation-extremely/)
- [The FLORES Evaluation Datasets for Low-Resource Machine Translation: Nepali–English and Sinhala–English](https://md.research.elamur.ai/free/en/papers/guzman-2019-flores-evaluation-datasets-low-resource/)
- [Achieving Human Parity on Automatic Chinese to English News Translation](https://md.research.elamur.ai/free/en/papers/hassan-2018-achieving-human-parity-automatic-chinese/)
- [Disentangled Representation Learning for Non-Parallel Text Style Transfer](https://md.research.elamur.ai/free/en/papers/john-2019-disentangled-representation-learning-non-parallel/)
- [Reformulating Unsupervised Style Transfer as Paraphrase Generation](https://md.research.elamur.ai/free/en/papers/krishna-2020-reformulating-unsupervised-style-transfer-as/)
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- [Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer](https://md.research.elamur.ai/free/en/papers/li-2018-delete-retrieve-generate-simple-approach/)
- [Pre-training Multilingual Neural Machine Translation by Leveraging Alignment Information](https://md.research.elamur.ai/free/en/papers/lin-2020-pre-training-multilingual-neural-machine/)
- [Event Extraction as Machine Reading Comprehension](https://md.research.elamur.ai/free/en/papers/liu-2020-event-extraction-as-machine-reading/)
- [A Dual Reinforcement Learning Framework for Unsupervised Text Style Transfer](https://md.research.elamur.ai/free/en/papers/luo-2019-dual-reinforcement-learning-framework-unsupervised/)
- [Multilingual and Multi-Aspect Hate Speech Analysis](https://md.research.elamur.ai/free/en/papers/ousidhoum-2019-multilingual-multi-aspect-hate-speech/)
- [Contrastive Learning for Many-to-many Multilingual Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/pan-2021-contrastive-learning-many-many-multilingual/)
- [Unsupervised Question Decomposition for Question Answering](https://md.research.elamur.ai/free/en/papers/perez-2020-unsupervised-question-decomposition-question-answering/)
- [True Few-Shot Learning with Language Models](https://md.research.elamur.ai/free/en/papers/perez-2021-true-few-shot-learning-language/)
- [Style Transfer Through Back-Translation](https://md.research.elamur.ai/free/en/papers/prabhumoye-2018-style-transfer-through-back-translation/)
- [BPE-Dropout: Simple and Effective Subword Regularization](https://md.research.elamur.ai/free/en/papers/provilkov-2020-bpe-dropout-simple-effective-subword/)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://md.research.elamur.ai/free/en/papers/reimers-2020-making-monolingual-sentence-embeddings-multilingual/)
- [Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer](https://md.research.elamur.ai/free/en/papers/santos-2018-fighting-offensive-language-social-media/)
- [Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing](https://md.research.elamur.ai/free/en/papers/schuster-2019-cross-lingual-alignment-contextual-word/)
- [On the Limitations of Unsupervised Bilingual Dictionary Induction](https://md.research.elamur.ai/free/en/papers/sogaard-2018-limitations-unsupervised-bilingual-dictionary-induction/)
- [MASS: Masked Sequence to Sequence Pre-training for Language Generation](https://md.research.elamur.ai/free/en/papers/song-2019-mass-masked-sequence-sequence-pre/)
- [Deciphering Undersegmented Ancient Scripts Using Phonetic Prior](https://md.research.elamur.ai/free/en/papers/tacl-2021-luo-deciphering-undersegmented-ancient-scripts-phonetic/)
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- [Unsupervised Text Style Transfer using Language Models as Discriminators](https://md.research.elamur.ai/free/en/papers/yang-2018-unsupervised-text-style-transfer-using/)
- [Style Transfer as Unsupervised Machine Translation](https://md.research.elamur.ai/free/en/papers/zhang-2018-style-transfer-unsupervised-machine-translation/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{lample2017unsupervised,
  title = {{Unsupervised Machine Translation Using Monolingual Corpora Only}},
  author = {Lample, Guillaume and Conneau, Alexis and Denoyer, Ludovic and Ranzato, Marc'Aurelio},
  year = {2017},
  doi = {10.48550/arxiv.1711.00043},
  eprint = {1711.00043},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1711.00043}
}
```

- [← previous Unsupervised AMR-Dependency Parse Alignment](https://md.research.elamur.ai/free/en/papers/chen-2017-unsupervised-amr-dependency-parse-alignment/)
- [next → Unsupervised Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/artetxe-2017-unsupervised-neural-machine-translation/)
- [↑ back to papers of 2017](https://md.research.elamur.ai/free/en/papers/2017/page/4/)

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