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Multilingual Universal Sentence Encoder For Semantic Retrieval.pdf

1907.04307.pdf
Preview of Multilingual Universal Sentence Encoder for Semantic Retrieval
🔗 Source: arxiv.org
📊 Size: 470 KB
📄 Pages: 6 pages
⬇️ Downloads: 580

Summary

Researchers introduce two pre-trained multilingual sentence encoding models, one based on the Transformer architecture and the other on CNN, which embed text from 16 languages into a single semantic space. The models are trained using a multi-task dual-encoder framework and achieve competitive performance on semantic retrieval, translation pair bitext retrieval, and retrieval question answering tasks. The models are made available for download on TensorFlow Hub and can be used for efficient nearest neighbor search. The supported languages include Arabic, Chinese, Dutch, English, French, German, Italian, Japanese, Korean, Polish, Portuguese, Russian, Spanish, Thai, and Turkish.

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Researchers introduce two pre-trained multilingual sentence encoding models, one based on the Transformer architecture and the other on CNN, which embed text...

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  • File Format: PDF
  • File Size: 470 KB
  • Pages: 6
  • Language: EN
  • Total Downloads: 580
  • Last Updated: 2 hours ago

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Multilingual guideMultilingual tutorialUniversal guideUniversal tutorialSentence guideSentence tutorialEncoder guideEncoder tutorialSemantic guideSemantic tutorialRetrieval guideRetrieval tutorial

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