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• W2963840929 abstract "This work presents state of the art results in reconstruction of surface realizations from obfuscated text. We identify the lack of sufficient training data as the major obstacle to training high-performing models, and solve this issue by generating large amounts of synthetic training data. We also propose preprocessing techniques which make the structure contained in the input features more accessible to sequence models. Our models were ranked first on all evaluation metrics in the English portion of the 2018 Surface Realization shared task." @default.
• W2963840929 created "2019-07-30" @default.
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• W2963840929 date "2018-01-01" @default.
• W2963840929 modified "2023-09-24" @default.
• W2963840929 title "Generating High-Quality Surface Realizations Using Data Augmentation and Factored Sequence Models" @default.
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• W2963840929 doi "https://doi.org/10.18653/v1/w18-3606" @default.
• W2963840929 hasPublicationYear "2018" @default.
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