Approximation-Aware Dependency Parsing by Belief Propagation
This work addresses the trade-off between speed and accuracy in dependency parsing for natural language processing, offering an incremental improvement by optimizing parameters to mitigate errors from approximate inference.
The paper tackles the problem of improving the accuracy of a fast dependency parser that uses approximate inference via loopy belief propagation, by training the model to compensate for approximation errors through gradient-based optimization, resulting in higher accuracy with fewer belief propagation iterations.
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 Stoyanov et al. (2011) and Domke (2010) did for loopy CRFs. The resulting trained parser obtains higher accuracy with fewer iterations of belief propagation than one trained by conditional log-likelihood.