Ranking and Reranking with Perceptron

Abstract

This work is inspired by the so-called reranking tasks in natural language processing. In this paper, we first study the ranking, reranking, and ordinal regression algorithms proposed recently in the context of ranks and margins. Then we propose a general framework for ranking and reranking, and introduce a series of variants of the perceptron algorithm for ranking and reranking in the new framework. Compared to the approach of using pairwise objects as training samples, the new algorithms reduces the data complexity and training time. We apply the new perceptron algorithms to the parse reranking and machine translation reranking tasks, and study the performance of reranking by employing various definitions of the margins.