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• W2740939229 abstract "This paper formalizes a sound extension of dynamic oracles to global training, in the frame of transition-based dependency parsers. By dispensing with the pre-computation of references, this extension widens the training strategies that can be entertained for such parsers; we show this by revisiting two standard training procedures, early-update and max-violation, to correct some of their search space sampling biases. Experimentally, on the SPMRL treebanks, this improvement increases the similarity between the train and test distributions and yields performance improvements up to 0.7 UAS, without any computation overhead." @default.
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• W2740939229 date "2017-01-01" @default.
• W2740939229 modified "2023-10-13" @default.
• W2740939229 title "Don't Stop Me Now! Using Global Dynamic Oracles to Correct Training Biases of Transition-Based Dependency Parsers" @default.
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• W2740939229 doi "https://doi.org/10.18653/v1/e17-2051" @default.
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