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• W2891836487 abstract "In this paper we advocate the use of bilingual corpora which are abundantly available for training sentence compression models. Our approach borrows much of its machinery from neural machine translation and leverages bilingual pivoting: compressions are obtained by translating a source string into a foreign language and then back-translating it into the source while controlling the translation length. Our model can be trained for any language as long as a bilingual corpus is available and performs arbitrary rewrites without access to compression specific data. We release. Moss, a new parallel Multilingual Compression dataset for English, German, and French which can be used to evaluate compression models across languages and genres." @default.
• W2891836487 created "2018-09-27" @default.
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• W2891836487 date "2018-01-01" @default.
• W2891836487 modified "2023-09-25" @default.
• W2891836487 title "Sentence Compression for Arbitrary Languages via Multilingual Pivoting" @default.
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• W2891836487 doi "https://doi.org/10.18653/v1/d18-1267" @default.
• W2891836487 hasPublicationYear "2018" @default.
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