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Learning Joint Multilingual Sentence Representations with Neural Machine Translation

arXiv 1704.04154 ↗First posted 13 Apr 2017cs.CLcited by 52

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Abstract (arXiv)

In this paper, we use the framework of neural machine translation to learn joint sentence representations across six very different languages. Our aim is that a representation which is independent of the language, is likely to capture the underlying semantics. We define a new cross-lingual similarity measure, compare up to 1.4M sentence representations and study the characteristics of close sentences. We provide experimental evidence that sentences that are close in embedding space are indeed semantically highly related, but often have quite different structure and syntax. These relations also hold when comparing sentences in different languages.

Key facts. Cited by 52 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Apr 2017.

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Citing papers per year: 2021: 13; 2022: 14; 2023: 10; 2024: 9; 2025: 4; 2026: 2.

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38 more
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  9. 2021Graph-based Multilingual Product Retrieval in E-commerce Searchcited by 9
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  11. 2021FST: the FAIR Speech Translation System for the IWSLT21 Multilingual Shared Taskcited by 6
  12. 2022NMTScore: A Multilingual Analysis of Translation-based Text Similarity Measurescited by 6
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  18. 2022Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrievalcited by 5
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  29. 2022EMS: Efficient and Effective Massively Multilingual Sentence Embedding Learningcited by 1
  30. 2023Are the Best Multilingual Document Embeddings simply Based on Sentence Embeddings?cited by 1
  31. 2024Mitigating Semantic Leakage in Cross-lingual Embeddings via Orthogonality Constraintcited by 1
  32. 2024Pralekha: Cross-Lingual Document Alignment for Indic Languagescited by 1
  33. 2025DOLFIN -- Document-Level Financial test set for Machine Translationcited by 1
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  36. 2023tmn at SemEval-2023 Task 9: Multilingual Tweet Intimacy Detection using XLM-T, Google Translate, and Ensemble Learningcited by 0
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