Paper: Loss Minimization In Parse Reranking

ACL ID W06-1666
Title Loss Minimization In Parse Reranking
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2006

We propose a general method for reranker construction which targets choosing the candidate with the least expected loss, rather than the most probable candidate. Different approaches to expected loss ap- proximation are considered, including es- timating from the probabilistic model used to generate the candidates, estimating from a discriminative model trained to rerank the candidates, and learning to ap- proximate the expected loss. The pro- posed methods are applied to the parse reranking task, with various baseline mod- els, achieving significant improvement both over the probabilistic models and the discriminative rerankers. When a neural network parser is used as the probabilistic model and the Voted Perceptron algorithm with data-defined kernels as the learning algorithm, the loss m...