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• W2891933973 abstract "To approximately parse an unfamiliar language, it helps to have a treebank of a similar language. But what if the closest available treebank still has the wrong word order? We show how to (stochastically) permute the constituents of an existing dependency treebank so that its surface part-of-speech statistics approximately match those of the target language. The parameters of the permutation model can be evaluated for quality by dynamic programming and tuned by gradient descent (up to a local optimum). This optimization procedure yields trees for a new artificial language that resembles the target language. We show that delexicalized parsers for the target language can be successfully trained using such “made to order” artificial languages." @default.
• W2891933973 created "2018-09-27" @default.
• W2891933973 creator A5043674345 @default.
• W2891933973 creator A5052467896 @default.
• W2891933973 date "2018-01-01" @default.
• W2891933973 modified "2023-09-23" @default.
• W2891933973 title "Synthetic Data Made to Order: The Case of Parsing" @default.
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• W2891933973 doi "https://doi.org/10.18653/v1/d18-1163" @default.
• W2891933973 hasPublicationYear "2018" @default.
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