Answers.Body: <p>You can try to read about MUSE (Multilingual Unsupervised and Supervised Embeddings) by Facebook. You can read it <a href="https://github.com/facebookresearch/MUSE" rel="nofollow noreferrer">from its Github</a> or <a href="https://code.fb.com/ml-applications/under-the-hood-multilingual-embeddings/" rel="nofollow noreferrer">this article</a>. They also provide the FastText dictionary format (.vec file) for some languages. </p>
  
  <p>Their <a href="https://arxiv.org/pdf/1710.04087.pdf" rel="nofollow noreferrer">original paper</a> shows how it aligns the vector of words from two different languages:</p>
  
  <p><a href="https://i.sstatic.net/zs1EF.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/zs1EF.png" alt="enter image description here"></a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-04-16T17:38:24.237Z
Answers.LastActivityDate: 2019-04-16T17:38:24.237Z
Answers.UserDisplayName: malioboro
Answers.Body: <p>For cross-language word representation the trend now is:</p>
  
  <ul>
  <li><strong>ELMoForManyLangs</strong>: <a href="https://github.com/HIT-SCIR/ELMoForManyLangs" rel="nofollow noreferrer">git_repo</a> <a href="https://arxiv.org/pdf/1802.05365.pdf" rel="nofollow noreferrer">original_paper_March_2018</a></li>
  <li><strong>MUSE by Facebook</strong>: <a href="https://github.com/facebookresearch/MUSE" rel="nofollow noreferrer">git_repo</a> <a href="https://arxiv.org/pdf/1710.04087.pdf" rel="nofollow noreferrer">original_paper_January_2018</a></li>
  </ul>
  
  <p>Remember that you can also do the task in 2 steps: 
  <strong>Translate</strong> the words to a reference language (e.g english), then <strong>represent</strong> each one of them using any word representation model (in the reference language). </p>
  
  <p>The 2-steps option is also good as specific-language word representation models are more accurate, and there are a bench of easy-to-use libraries for single-language translation (<em>i.e <a href="https://pypi.org/project/py-translator/" rel="nofollow noreferrer">py-translator</a></em>) and representation (<em>i.e <a href="https://tfhub.dev/google/universal-sentence-encoder/2" rel="nofollow noreferrer">Universal sentence encoder by Google</a></em>).    </p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-04-18T15:00:27.223Z
Answers.LastActivityDate: 2019-04-18T15:00:27.223Z
Answers.UserDisplayName: HLeb
Body: <p>Do we have cross-language vector space for word embedding?</p>
  
  <p>When measure similarity for apple/Pomme/mela/Lacus/苹果/りんご, they should be the same</p>
  
  <p>If would be great if there's available internet service of neuron network which already be trained by multiple language</p>
  
Tags: <natural-language-processing><word-embedding>
Title: Do we have cross-language vector space for word embedding?
UserDisplayName: gloomyson
ThreadText: # Do we have cross-language vector space for word embedding?
  
  (Asked by: username_0 on 2019-04-15)
  
  Do we have cross-language vector space for word embedding?
  
  When measure similarity for apple/Pomme/mela/Lacus/苹果/りんご, they should be the same
  
  If would be great if there's available internet service of neuron network which already be trained by multiple language
  
  **Tags:** natural-language-processing, word-embedding
  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2019-04-16. Score: 2
  
  You can try to read about MUSE (Multilingual Unsupervised and Supervised Embeddings) by Facebook. You can read it [from its Github](https://github.com/facebookresearch/MUSE) or [this article](https://code.fb.com/ml-applications/under-the-hood-multilingual-embeddings/). They also provide the FastText dictionary format (.vec file) for some languages.
  
  Their [original paper](https://arxiv.org/pdf/1710.04087.pdf) shows how it aligns the vector of words from two different languages:
  
  [![enter image description here](https://i.sstatic.net/zs1EF.png)](https://i.sstatic.net/zs1EF.png)
  
  
  ---
  
  ### Answer by username_2 on 2019-04-18. Score: 1
  
  For cross-language word representation the trend now is:
  
  * **ELMoForManyLangs**: [git\_repo](https://github.com/HIT-SCIR/ELMoForManyLangs) [original\_paper\_March\_2018](https://arxiv.org/pdf/1802.05365.pdf)
  * **MUSE by Facebook**: [git\_repo](https://github.com/facebookresearch/MUSE) [original\_paper\_January\_2018](https://arxiv.org/pdf/1710.04087.pdf)
  
  Remember that you can also do the task in 2 steps:
  **Translate** the words to a reference language (e.g english), then **represent** each one of them using any word representation model (in the reference language).
  
  The 2-steps option is also good as specific-language word representation models are more accurate, and there are a bench of easy-to-use libraries for single-language translation (*i.e [py-translator](https://pypi.org/project/py-translator/)*) and representation (*i.e [Universal sentence encoder by Google](https://tfhub.dev/google/universal-sentence-encoder/2)*).
  
  
  ---
  
  