---
title: "Learning Joint Multilingual Sentence — Schwenk 2017"
description: "The paper asks whether neural machine translation can act as a training signal for sentence embeddings that do not depend on the input language."
authors: ["Holger Schwenk", "Matthijs Douze"]
year: 2017
venue: "Proceedings of the 2nd Workshop on Representation Learning for NLP"
source: "https://aclanthology.org/W17-2619/"
doi: "10.18653/v1/w17-2619"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/schwenk-2017-learning-joint-multilingual-sentence-representations/"
---
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# Learning Joint Multilingual Sentence Representations with Neural Machine Translation

**Authors:** [Holger Schwenk](https://md.research.elamur.ai/free/en/authors/holger-schwenk/), [Matthijs Douze](https://md.research.elamur.ai/free/en/authors/matthijs-douze/) · **Year:** 2017 · **Venue:** Proceedings of the 2nd Workshop on Representation Learning for NLP · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/W17-2619/) · [PDF](https://aclanthology.org/W17-2619.pdf) · [DOI 10.18653/v1/w17-2619](https://doi.org/10.18653/v1/w17-2619) · [OpenAlex](https://openalex.org/W2607106700)

**Citations:** 197 citations (openalex-snapshot · 2026-10-14) · [98](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper asks whether neural machine translation can act as a training signal for sentence embeddings that do not depend on the input language. The authors build an NMT system with one encoder and one decoder per language and compress each sentence into a single fixed-size vector. They drop the usual attention mechanism, so the decoders can be discarded after training and only the encoders remain. They describe a family of "partial training paths" (1:1, M:1, 1:N, M:N). Each path decides, per mini-batch, which encoders receive input and which decoders receive targets, and the authors discuss the parallel data each one needs. To measure quality, they define a cross-lingual similarity error rate that checks whether translations end up next to each other. Across six typologically diverse languages, the average error over all 21 language pairs is 1.2%. They also study how this changes when the search pool grows to 1.4 million out-of-domain sentences. Nearest neighbours turn out to be paraphrases with different wording, both within and across languages. This points to uses in multilingual search and cross-lingual transfer.

## Key points
- Six languages are covered: English, French, Spanish, Russian, Arabic and Chinese. Together they give 21 language pairs, and the cross-lingual similarity error averaged across them comes to 1.2%.
- Retrieval is scaled up to 1.4M sentences drawn from an out-of-domain corpus. Qualitative search examples (monolingual and EN→FR/ES) come from Europarl and use cosine similarity.
- The architecture has L encoders and L decoders and uses fixed-size sentence vectors without attention. Self-attention or inner-attention are mentioned as possible input-only alternatives.
- Training strategies differ in their data needs:
  - 1:1 with a shared target, or M:1 with averaged embeddings, needs only bitexts with a pivot language. However, the pivot language itself never gets an embedding.
  - 1:N, which translates into all other languages without autoencoding, gives embeddings for every language but requires L-way parallel data such as Europarl, TED or UN.
- Close neighbours in embedding space are semantically related but often differ strongly in syntax and structure.

## Interesting ideas
- **Shared decoder as an alignment pressure:** routing many encoders into one shared decoder of limited capacity pushes all languages toward a common representation. This is a cheap way to align languages without an explicit distance loss.
- **Per-mini-batch partial training paths:** the active encoders and decoders can change from batch to batch, which lets one model mix bitexts, multi-way parallel text and even monolingual data. This is useful when a low-resource language such as Ukrainian has uneven parallel coverage.
- **Avoid trivial autoencoding:** feeding the target language back as input turns translation into an easier copying task, which may weaken the representation. Excluding the source language from the targets keeps the objective meaningful.
- **Evaluate embeddings by translation retrieval:** checking whether a sentence's translation is its nearest neighbour gives a simple, language-agnostic quality metric. Growing the search pool to millions of distractors adds a realistic stress test for any multilingual encoder.
- **Inspect the score falloff qualitatively:** listing the top neighbours with their cosine scores shows how similarity drops from exact translations to partial topical matches. This helps set retrieval thresholds, for example when mining parallel sentences.

*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: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

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

## Cited by

Cited by

98 papers of our library cite it (of the 140884 whose bibliographies we read); 4 of them have a page here, 94 not yet.

- [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://md.research.elamur.ai/free/en/papers/artetxe-2018-massively-multilingual-sentence-embeddings-zero/)
- [A Corpus for Multilingual Document Classification in Eight Languages](https://md.research.elamur.ai/free/en/papers/schwenk-2018-corpus-multilingual-document-classification-eight/)
- [Automatic Machine Translation Evaluation in Many Languages via Zero-Shot Paraphrasing](https://md.research.elamur.ai/free/en/papers/thompson-2020-automatic-machine-translation-evaluation-many/)
- [Paraphrase Generation as Zero-Shot Multilingual Translation: Disentangling Semantic Similarity from Lexical and Syntactic Diversity](https://md.research.elamur.ai/free/en/papers/thompson-2020-paraphrase-generation-as-zero-shot/)

## Cite

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

```bibtex
@inproceedings{schwenk2017learning,
  title = {{Learning Joint Multilingual Sentence Representations with Neural Machine Translation}},
  author = {Schwenk, Holger and Douze, Matthijs},
  booktitle = {Proceedings of the 2nd Workshop on Representation Learning for NLP},
  year = {2017},
  doi = {10.18653/v1/w17-2619},
  url = {https://doi.org/10.18653/v1/w17-2619}
}
```

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## References

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39. M Yonghui Wu et al. 2016. Google’s neural machine translation system: Bridging the gap between human Barret Zoph and Kevin Knight. 2016. Multi-source neural translation. In NAACL. pages 30–34. [find](https://scholar.google.com/scholar?q=M+Yonghui+Wu+et+al%2E+2016%2E+Google%E2%80%99s+neural+machine+translation+system%3A+Bridging+the+gap+between+human+Barret+Zoph+and+Kevin+Knight%2E+2016%2E+Multi%2Dsource+neural+translation%2E+In+NAACL%2E+pages+30%E2%80%9334%2E) [our card](https://md.research.elamur.ai/free/en/papers/zoph-2016-multi-source-neural-translation/)
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