This paper proposes the syntactic category prediction for improving translation quality. In parsing using sentence segmentation, the segments are separately parsed and then the parsing results of each segment are combined to generate a global sentence structure. The syntactic category prediction guides the parser to identify relationships among segments and to select the correct parsing results for each segment. We design features for predicting syntactic categories and generate decision trees for the prediction using training data from the Penn Treebank. In experiment, we show the prediction accuracy and comparison results with the prediction by human-built rules, heuristic probability function, and neural networks. Also, we present how much the category prediction contributes to improving translation quality.