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• W1940741243 abstract "We show how to train the fast dependency parser of Smith and Eisner (2008) for improved accuracy. This parser can consider higher-order interactions among edges while retaining O( n 3 ) runtime. It outputs the parse with maximum expected recall—but for speed, this expectation is taken under a posterior distribution that is constructed only approximately, using loopy belief propagation through structured factors. We show how to adjust the model parameters to compensate for the errors introduced by this approximation, by following the gradient of the actual loss on training data. We find this gradient by back-propagation. That is, we treat the entire parser (approximations and all) as a differentiable circuit, as others have done for loopy CRFs (Domke, 2010; Stoyanov et al., 2011; Domke, 2011; Stoyanov and Eisner, 2012). The resulting parser obtains higher accuracy with fewer iterations of belief propagation than one trained by conditional log-likelihood." @default.
• W1940741243 created "2016-06-24" @default.
• W1940741243 creator A5024437840 @default.
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• W1940741243 date "2015-12-01" @default.
• W1940741243 modified "2023-09-23" @default.
• W1940741243 title "Approximation-Aware Dependency Parsing by Belief Propagation" @default.
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• W1940741243 doi "https://doi.org/10.1162/tacl_a_00153" @default.
• W1940741243 hasPublicationYear "2015" @default.
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