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• W2963672008 abstract "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." @default.
• W2963672008 created "2019-07-30" @default.
• W2963672008 creator A5015857371 @default.
• W2963672008 creator A5053822883 @default.
• W2963672008 date "2017-01-01" @default.
• W2963672008 modified "2023-10-01" @default.
• W2963672008 title "Learning Joint Multilingual Sentence Representations with Neural Machine Translation" @default.
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• W2963672008 doi "https://doi.org/10.18653/v1/w17-2619" @default.
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