Guest editorial: special issue on structured prediction

Abstract

Structured Prediction or Structured Classification (Bakir et al. 2007) is the task of predicting a collection of related variables given some input. The relationship between the variables to be predicted is often complex. An example of such complex dependencies is machine translation, where the input is a sequence of words in the source natural language and the output is a sequence of words in the target natural language. Here, each word in the target language relates not only to the words in the source language, but also to the other (arbitrarily far) words in the target sequence. As a field, structured prediction has some unique challenges, several of which are addressed by the papers in this issue. One of the most obvious of these is that the output spaces in question are often exponential in size, and so the complexity, both of these spaces and the learned models, can result in computationally infeasible learning and inference algorithms. In many cases, therefore, efficient optimization remains an open problem in structured prediction. Two papers in this special issue (Hsu et al. 2009; Sutton and McCallum 2009) address this problem. An important source of complexity in structured prediction algorithms is in the iterative nature of the training step: Often, training is done EM-style, where model parameters are estimated at each step, and then inference is performed based on these parameters. In models with complex structure, this inference step