A Continuous Space Rule Selection Model for Syntax-based Statistical Machine Translation

Abstract

One of the major challenges for statistical machine translation (SMT) is to choose the appropriate translation rules based on the sentence context. This paper proposes a continuous space rule selection (CSRS) model for syntax-based SMT to perform this context-dependent rule selection. In contrast to existing maximum entropy based rule selection (MERS) models, which use discrete representations of words as features, the CSRS model is learned by a feed-forward neural network and uses real-valued vector representations of words, allowing for better generalization. In addition, we propose a method to train the rule selection models only on minimal rules, which are more frequent and have richer training data compared to non-minimal rules. We tested our model on different translation tasks and the CSRS model outperformed a baseline without rule selection and the previous MERS model by up to 2.2 and 1.1 points of BLEU score respectively.