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• W2251656952 abstract "A major challenge in supervised sentence compression is making use of rich feature representations because of very scarce parallel data. We address this problem and present a method to automatically build a compression corpus with hundreds of thousands of instances on which deletion-based algorithms can be trained. In our corpus, the syntactic trees of the compressions are subtrees of their uncompressed counterparts, and hence supervised systems which require a structural alignment between the input and output can be successfully trained. We also extend an existing unsupervised compression method with a learning module. The new system uses structured prediction to learn from lexical, syntactic and other features. An evaluation with human raters shows that the presented data harvesting method indeed produces a parallel corpus of high quality. Also, the supervised system trained on this corpus gets high scores both from human raters and in an automatic evaluation setting, significantly outperforming a strong baseline." @default.
• W2251656952 created "2016-06-24" @default.
• W2251656952 creator A5037657908 @default.
• W2251656952 creator A5085471281 @default.
• W2251656952 date "2013-10-01" @default.
• W2251656952 modified "2023-09-23" @default.
• W2251656952 title "Overcoming the Lack of Parallel Data in Sentence Compression" @default.
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