Projects per year
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 release1 MOSS, a new parallel Multilingual Compression dataset for English, German, and French which can be used to evaluate compression models across languages and genres.
|Title of host publication||2018 Conference on Empirical Methods in Natural Language Processing|
|Place of Publication||Brussels, Belgium|
|Publisher||Association for Computational Linguistics|
|Number of pages||12|
|Publication status||Published - Nov 2018|
|Event||2018 Conference on Empirical Methods in Natural Language Processing - Square Meeting Center, Brussels, Belgium|
Duration: 31 Oct 2018 → 4 Nov 2018
|Conference||2018 Conference on Empirical Methods in Natural Language Processing|
|Abbreviated title||EMNLP 2018|
|Period||31/10/18 → 4/11/18|
FingerprintDive into the research topics of 'Sentence Compression for Arbitrary Languages via Multilingual Pivoting'. Together they form a unique fingerprint.
- 1 Finished
TransModal: Translating from Multiple Modalities into Text
1/09/16 → 31/08/22