A Learning Error Analysis for Structured Prediction with Approximate Inference

Yuanbin Wu, Man Lan, Shiliang Sun, Qi Zhang, Xuanjing Huang

Abstract: In this work, we try to understand the differences between exact and approximate inference algorithms in structured prediction. We compare the estimation and approximation error of both underestimate and overestimate models. The result shows that, from the perspective of learning errors, performances of approximate inference could be as good as exact inference. The error analyses also suggest a new margin for existing learning algorithms. Empirical evaluations on text classification, sequential labelling and dependency parsing witness the success of approximate inference and the benefit of the proposed margin.